Carbon Chemistry analysis: seasonality and long trends
Ana Palacio-Castro
2023-03-26
Libraries and settings
1. Import and format carbon chemistry data
Seasonality
- Winter: Jan-Mar
- Spring: Apr-Jun
- Summer: Jul-Sep
- Fall: Oct-Dec
- Wet: May - October
- Dry: November - April
2. Check data available
2.1 Locations (all samples)
Figure Supp: Map of the sites where individual samples were collected
Notes: * Check location of individual SiteIDs
2.2 Average GPS point location
Flag samples with coordinates that seem off from the permanent station
# Coordinates<-select(FLK.data, "CTDID", "Latitude", "Longitude", "SiteID")
# Coordinates<-join(Coordinates, WS.GPS.Sites,
# type = "left", by="SiteID")
# Coordinates$Lat_diff <- abs(Coordinates$Latitude-Coordinates$Lat)
# Coordinates$Lon_diff <- abs(Coordinates$Longitude-Coordinates$Lon)
# Check.coordinates1 <- Coordinates[(Coordinates$Lat_diff>0.02), ]
# Check.coordinates2 <- Coordinates[(Coordinates$Lon_diff>0.02), ]
# Check.coordinates <- rbind(Check.coordinates1, Check.coordinates2)
# Check.coordinates <- unique(Check.coordinates)
# #write.csv(Check.coordinates, "FLK_results/2_WrongGPS_points.csv", row.names=F)- Figure 1: Stations
Figure 1: Location of the permanent sampling stations along the Florida Reef Tract. The black boxes delimitate the biogeographical regions. The color of the stations represents their position along the shelf.
2.3 Number of samples
By zone and location
| Sub_region | Inshore | Mid channel | Offshore | Oceanic |
|---|---|---|---|---|
| BB | 83 | 75 | 82 | |
| UK | 105 | 70 | 110 | 36 |
| MK | 124 | 126 | 127 | 78 |
| LK | 133 | 136 | 168 | 85 |
By year and sub_region
| Sub_region | 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 |
|---|---|---|---|---|---|---|---|---|---|---|---|
| BB | 4 | 11 | 3 | 6 | 30 | 36 | 30 | 35 | 24 | 24 | 37 |
| UK | 7 | 15 | 3 | 8 | 45 | 51 | 42 | 44 | 30 | 31 | 45 |
| MK | 15 | 39 | 9 | 11 | 55 | 66 | 53 | 56 | 43 | 44 | 64 |
| LK | 18 | 51 | 9 | 11 | 58 | 72 | 59 | 76 | 53 | 45 | 70 |
By climatological season and region
| Sub_region | Fall | Spring | Winter | Summer |
|---|---|---|---|---|
| LK | 149 | 133 | 123 | 117 |
| MK | 132 | 119 | 95 | 109 |
| UK | 88 | 81 | 72 | 80 |
| BB | 68 | 60 | 52 | 60 |
By Month-year
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Months |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 38 | 37 | 19 | 6 | 35 | January | ||||||
| 16 | 22 | 37 | February | ||||||||
| 24 | 2 | 38 | 35 | 33 | March | ||||||
| 23 | 38 | 15 | 35 | 37 | April | ||||||
| 38 | 16 | 28 | May | ||||||||
| 20 | 24 | 38 | 38 | 35 | 8 | June | |||||
| 37 | 38 | 17 | 36 | 25 | July | ||||||
| 19 | 38 | 34 | 10 | August | |||||||
| 38 | 38 | 36 | September | ||||||||
| 28 | 38 | 18 | 38 | 36 | October | ||||||
| 37 | 35 | 37 | November | ||||||||
| 25 | 36 | 37 | 37 | 35 | December |
By Season-year
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | Season |
|---|---|---|---|---|---|---|---|---|---|---|---|
| 24 | 16 | 24 | 76 | 72 | 52 | 6 | 35 | 37 | Winter | ||
| 20 | 47 | 76 | 38 | 38 | 66 | 35 | 73 | Spring | |||
| 75 | 76 | 36 | 38 | 72 | 34 | 35 | Summer | ||||
| 53 | 36 | 37 | 35 | 38 | 55 | 37 | 75 | 71 | Fall |
By Month-region
| Sub_region | Zone | Jan | Feb | Mar | Apr | May | Jun | Jul | Aug | Sep | Oct | Nov | Dec |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| BB | Inshore | 7 | 4 | 7 | 9 | 4 | 8 | 10 | 4 | 6 | 8 | 6 | 10 |
| BB | Mid channel | 7 | 3 | 6 | 9 | 4 | 5 | 10 | 4 | 6 | 7 | 6 | 8 |
| BB | Offshore | 7 | 4 | 7 | 9 | 4 | 8 | 10 | 4 | 6 | 8 | 6 | 9 |
| UK | Inshore | 10 | 4 | 9 | 13 | 6 | 8 | 13 | 6 | 7 | 9 | 9 | 11 |
| UK | Mid channel | 7 | 2 | 7 | 5 | 3 | 9 | 9 | 3 | 6 | 6 | 5 | 8 |
| UK | Offshore | 12 | 3 | 9 | 11 | 5 | 12 | 14 | 5 | 9 | 11 | 8 | 11 |
| UK | Oceanic | 3 | 2 | 4 | 3 | 1 | 5 | 4 | 1 | 3 | 4 | 2 | 4 |
| MK | Inshore | 9 | 6 | 9 | 12 | 8 | 15 | 12 | 8 | 9 | 13 | 9 | 14 |
| MK | Mid channel | 9 | 7 | 11 | 12 | 8 | 13 | 13 | 8 | 9 | 13 | 9 | 14 |
| MK | Offshore | 10 | 6 | 11 | 13 | 7 | 13 | 13 | 8 | 9 | 13 | 9 | 15 |
| MK | Oceanic | 6 | 4 | 7 | 6 | 4 | 8 | 8 | 6 | 6 | 8 | 5 | 10 |
| LK | Inshore | 11 | 8 | 12 | 12 | 7 | 16 | 9 | 10 | 9 | 15 | 9 | 15 |
| LK | Mid channel | 12 | 9 | 12 | 12 | 7 | 15 | 10 | 11 | 9 | 15 | 9 | 15 |
| LK | Offshore | 18 | 7 | 13 | 14 | 10 | 18 | 13 | 15 | 12 | 19 | 12 | 17 |
| LK | Oceanic | 7 | 6 | 8 | 8 | 4 | 10 | 5 | 8 | 6 | 9 | 5 | 9 |
Per station during all sampling time
| 2010 | 2011 | 2012 | 2014 | 2015 | 2016 | 2017 | 2018 | 2019 | 2020 | 2021 | SiteID | Sub_region | Zone |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 1 | BB | Inshore |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 3 | BB | Offshore |
| 1 | 3 | 1 | 5 | 6 | 5 | 4 | 2 | 3 | 4 | 4 | UK | Inshore | |
| 1 | 3 | 1 | 1 | 5 | 6 | 5 | 4 | 2 | 3 | 4 | 5 | UK | Inshore |
| 2 | 2 | 1 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 4 | 5.5 | UK | Mid channel |
| 2 | 3 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 5 | 6 | UK | Offshore | |
| 1 | 4 | 1 | 1 | 5 | 5 | 4 | 4 | 3 | 3 | 5 | 6.5 | UK | Oceanic |
| 2 | 4 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 5 | 10 | MK | Inshore |
| 1 | 3 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 | 13 | MK | Inshore |
| 2 | 5 | 1 | 5 | 6 | 3 | 6 | 4 | 4 | 6 | 7 | MK | Inshore | |
| 2 | 3 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 11 | MK | Mid channel |
| 1 | 2 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 14 | MK | Mid channel |
| 2 | 4 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 8 | MK | Mid channel |
| 1 | 4 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 12 | MK | Offshore |
| 2 | 5 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 9 | MK | Offshore | |
| 2 | 4 | 1 | 1 | 5 | 6 | 5 | 4 | 3 | 4 | 5 | 9.5 | MK | Oceanic |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 3 | 5 | 16 | LK | Inshore |
| 2 | 5 | 1 | 5 | 6 | 4 | 6 | 4 | 4 | 6 | 19 | LK | Inshore | |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 24 | LK | Inshore |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 7 | 4 | 4 | 5 | 17 | LK | Mid channel |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 20 | LK | Mid channel |
| 1 | 5 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 23 | LK | Mid channel |
| 2 | 5 | 1 | 1 | 5 | 6 | 5 | 7 | 4 | 4 | 6 | 18 | LK | Offshore |
| 1 | 4 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 21LK | LK | Offshore | |
| 1 | 2 | 1 | 1 | 5 | 6 | 5 | 7 | 5 | 4 | 6 | 22 | LK | Offshore |
| 2 | 5 | 1 | 1 | 4 | 6 | 5 | 5 | 4 | 3 | 6 | 21.5 | LK | Oceanic |
| 1 | 5 | 1 | 1 | 4 | 6 | 5 | 6 | 5 | 3 | 6 | 22.5 | LK | Oceanic |
| 1 | 1 | 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | 2 | BB | Mid channel | |
| 3 | 1 | 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | 15 | MK | Offshore | |
| 2 | 1 | 1 | 5 | 6 | 5 | 4 | 4 | 4 | 6 | 15.5 | MK | Oceanic | |
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 7 | EK_IN | BB | Inshore | |||
| 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | EK_MID | BB | Mid channel | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | EK_OFF | BB | Offshore | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 5 | UK_IN | UK | Inshore | |||
| 1 | 5 | 6 | 5 | 5 | 4 | 4 | 6 | UK_MID | UK | Mid channel | |||
| 1 | 5 | 6 | 5 | 6 | 4 | 4 | 6 | UK_OFF | UK | Offshore | |||
| 5 | 6 | 5 | 7 | 5 | 4 | 6 | MR | UK | Offshore | ||||
| 5 | 6 | 5 | 7 | 5 | 4 | 6 | WS | LK | Offshore |
Remove extreme events?
# Label and filter extreme events
FLK.data$Extreme<-as.factor(FLK.data$Extreme)
summary(FLK.data$Extreme)## ColdMortality Low_pH LowSalinty Normal
## 24 109 28 1377 FLK.data_filtered<-FLK.data[FLK.data$Extreme=="Normal",]3. TA and DIC salinity normalization
Salinity standardizations
- Uses salinity Bottle first, if not available, uses salinity from CTD
#MeanSalinity<-mean(FLK.data[FLK.data$Zone=="Oceanic",]$BestSalinity)
MeanSalinity<-mean(FLK.data$BestSalinity)
sdSalinity<-sd(FLK.data$BestSalinity)
Sal<-paste("Mean salinity = ", round(MeanSalinity, digits=3),
"(", round(sdSalinity, digits=3), "sd)")
Sal## [1] "Mean salinity = 35.998 ( 0.796 sd)"TA_Sal<- ggplot(FLK.data) + MyTheme+
geom_point(aes (BestSalinity, TA_umol_kg, fill=Season),
alpha=0.5, shape=21, size=1)+
geom_smooth(aes(BestSalinity, TA_umol_kg),
linetype=2, method = "lm", color="black")
ggExtra::ggMarginal(
p = TA_Sal,
type = 'boxplot',
margins = 'both',
size = 5,
colour = 'black',
fill = 'gray'
)
TA_Sal + facet_wrap(~Zone)
TA_Sal+ facet_grid(Zone~Season)
#TA_Sal+ facet_grid(Zone~Precipitation)
DIC_Sal<- ggplot(FLK.data) + MyTheme+
geom_point(aes (BestSalinity, DIC_umol_kg, fill=Season),
alpha=0.5, shape=21, size=1)+
geom_smooth(aes(BestSalinity, DIC_umol_kg),
linetype=2, method = "lm", color="black")
ggExtra::ggMarginal(
p = DIC_Sal,
type = 'boxplot',
margins = 'both',
size = 5,
colour = 'black',
fill = 'gray'
)
DIC_Sal + facet_wrap(~Zone)
DIC_Sal+ facet_grid(Zone~Season)
- Calculate TA intercepts
Note: Use Model II Linear regressions!
“For Model II regressions, neither X nor Y is an INDEPENDENT variable but both are assumed to be DEPENDENT on some other parameter which is often unknown. Neither are”controlled”, both are measured, and both include some error. We do not seek an equation of how Y varies in response to a change in X, but rather we look for how they both co-vary in time or space in response to some other variable or process.”
- Use ordinary least squares (OLS)?
All data
##
## Model II regression
##
## Call: lmodel2(formula = TA_umol_kg ~ BestSalinity, data =
## FLK.data_filtered)
##
## n = 1374 r = -0.2278987 r-square = 0.05193781
## Parametric P-values: 2-tailed = 1.202032e-17 1-tailed = 6.010158e-18
## Angle between the two OLS regression lines = 2.667424 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 3097.785 -20.34694 -87.18632 NA
## 2 MA 16500.250 -391.70923 -89.85373 NA
## 3 SMA 5585.602 -89.28064 -89.35828 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 2931.598 3263.973 -24.95088 -15.74300
## 2 MA 13891.723 20634.460 -506.26203 -319.43084
## 3 SMA 5423.727 5756.040 -94.00321 -84.79532
##
## Eigenvalues: 3868.259 0.4600788
##
## H statistic used for computing C.I. of MA: 3.336788e-07
Oceanic
##
## Model II regression
##
## Call: lmodel2(formula = TA_umol_kg ~ BestSalinity, data =
## FLK.data_filtered[FLK.data_filtered$Zone == "Oceanic", ])
##
## n = 176 r = 0.3257975 r-square = 0.106144
## Parametric P-values: 2-tailed = 1.022381e-05 1-tailed = 5.111904e-06
## Angle between the two OLS regression lines = 7.094668 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 2122.28183 7.166943 82.05683 NA
## 2 MA -48.06268 67.396258 89.14993 NA
## 3 SMA 1587.84376 21.998155 87.39722 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 2010.133 2234.4303 4.05505 10.27884
## 2 MA -1911.555 687.2507 46.99055 119.11010
## 3 SMA 1467.815 1692.0877 19.10528 25.32906
##
## Eigenvalues: 139.7934 0.2581017
##
## H statistic used for computing C.I. of MA: 4.148763e-05
- Calculate DIC intercepts
All data
##
## Model II regression
##
## Call: lmodel2(formula = DIC_umol_kg ~ BestSalinity, data =
## FLK.data_filtered)
##
## n = 1367 r = -0.3836313 r-square = 0.1471729
## Parametric P-values: 2-tailed = 3.640591e-49 1-tailed = 1.820295e-49
## Angle between the two OLS regression lines = 1.280546 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 3409.339 -38.14802 -88.49841 NA
## 2 MA 11386.282 -259.18306 -89.77894 NA
## 3 SMA 5621.283 -99.43930 -89.42383 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 3233.340 3585.339 -43.02393 -33.27211
## 2 MA 10326.227 12757.025 -297.16533 -229.80975
## 3 SMA 5449.627 5801.561 -104.43468 -94.68286
##
## Eigenvalues: 4816.519 0.4153987
##
## H statistic used for computing C.I. of MA: 2.431873e-07
Oceanic
##
## Model II regression
##
## Call: lmodel2(formula = DIC_umol_kg ~ BestSalinity, data =
## FLK.data_filtered[FLK.data_filtered$Zone == "Oceanic", ])
##
## n = 174 r = 0.3149479 r-square = 0.09919215
## Parametric P-values: 2-tailed = 2.308735e-05 1-tailed = 1.154368e-05
## Angle between the two OLS regression lines = 4.949134 degrees
##
## Regression results
## Method Intercept Slope Angle (degrees) P-perm (1-tailed)
## 1 OLS 1669.8245 10.39310 84.50405 NA
## 2 MA -1728.0070 104.69073 89.45273 NA
## 3 SMA 855.2498 32.99942 88.26426 NA
##
## Confidence intervals
## Method 2.5%-Intercept 97.5%-Intercept 2.5%-Slope 97.5%-Slope
## 1 OLS 1499.9523 1839.697 5.679278 15.10691
## 2 MA -4858.8657 -550.885 72.022893 191.57926
## 3 SMA 673.3265 1013.033 28.620573 38.04821
##
## Eigenvalues: 317.9112 0.2629337
##
## H statistic used for computing C.I. of MA: 1.876549e-05
- Add TA and DIC endmembers (S=0) to data frame
# 4. Add endmember intercepts
TA_SO<-subset(TA_coef, term=="(Intercept)")
TA_SO<-as.data.frame(dplyr::select(TA_SO, c(Region, estimate)))
names(TA_SO)[2] <- "TA_0"
DIC_SO<-subset(DIC_coef, term=="(Intercept)")
DIC_SO<-as.data.frame(dplyr::select(DIC_SO, c(Region, estimate)))
names(DIC_SO)[2] <- "DIC_0"
Intercepts<-join(TA_SO, DIC_SO, by="Region", type="full")
FLK.data<-join(FLK.data, Intercepts, by="Region", type="left")- Normalize with (1) Friss equation and (2) mean salinity
# 5. endmember and salinity (mean) normalization
FLK.data$nTA<-((
(FLK.data$TA_umol_kg-FLK.data$TA_0)/
FLK.data$BestSalinity)*MeanSalinity)+FLK.data$TA_0
FLK.data$nDIC<-((
(FLK.data$DIC_umol_kg-FLK.data$DIC_0)/
FLK.data$BestSalinity)*MeanSalinity)+FLK.data$DIC_0
# 2. TA and DIC 35 normalization
FLK.data$n35TA<-(FLK.data$TA_umol_kg/FLK.data$BestSalinity)*MeanSalinity
FLK.data$n35DIC<-(FLK.data$DIC_umol_kg/FLK.data$BestSalinity)*MeanSalinity
# FLK.data$n35TA<-(FLK.data$TA_umol_kg/FLK.data$BestSalinity)*35
# FLK.data$n35DIC<-(FLK.data$DIC_umol_kg/FLK.data$BestSalinity)*35
#write.csv(FLK.data, "FLK_results/FLK.data_filtered_normalized.csv", row.names = F)- TA parameters to nTA (S=0)
kable(as.data.frame(TA_coef[,1:8], format = "markdown"),
caption = "Table 1: TA parameters for salinity normalization with endmember S=0",
digits = 3)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 2122.282 | 56.822 | 37.350 | 0 | 0.106 | 0.101 |
| FL | BestSalinity | 7.167 | 1.577 | 4.546 | 0 | 0.106 | 0.101 |
#write.csv(TA_coef, "TA_coef.csv")# Plot
Normalization_plot<- ggplot(FLK.data) +
theme_bw() +
theme(legend.position="bottom",
plot.background=element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.major.x = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
legend.box.background = element_rect(),
legend.title = element_blank(),
panel.background =element_rect(fill = NA,
color = "black"))+
scale_x_continuous(limits = c(31.5, 39),
expand = c(0.05, 0.05),
breaks = seq(30, 40, 2),
name=expression(paste("Salinity")))- DIC parameters to nDIC (S=0)
kable(as.data.frame(DIC_coef[,1:8], format = "markdown"),
caption = "Table 2: DIC parameters for salinity normalization with endmember S0",
digits = 2) %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Region | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|
| FL | (Intercept) | 1669.82 | 86.06 | 19.40 | 0 | 0.1 | 0.09 |
| FL | BestSalinity | 10.39 | 2.39 | 4.35 | 0 | 0.1 | 0.09 |
#write.csv(DIC_coef, "DIC_coef.csv")
Figure Supp 1: Overview of linear regressions of total alkalinity (TA, µmol kg−1) and dissolved inorganic carbon (DIC, µmol kg−1) as a function of salinity + Friss normalized values.
- Original data: Black line and circles
- Friis normalization S0 end-member: purple line and pointing up triangles
- Mean salinity normalization: purple line and pointing down triangles
TA vs Friss salinity normalized TA
Figure Supp X: Linear regressions of Friss normalized total alkalinity (nTA, µmol kg−1) as a function of measured Total Alcalinity (TA, µmol kg−1)
DIC vs Friss salinity normalized DIC
Figure Supp X: Linear regressions of Friss normalized Dissolved Inorganc Carbon (nDIC, µmol kg−1) as a function of measured Dissolved Inorganc Carbon (DIC, µmol kg−1)
TA vs Salinity normalized TA
Figure Supp X: Linear regressions of mean salinity normalized total alkalinity (n35TA, µmol kg−1) as a function of measured Total Alcalinity (TA, µmol kg−1)
DIC vs Salinity normalized DIC
Figure Supp X: Linear regressions of mean salinity normalized Dissolved Inorganc Carbon (n35DIC, µmol kg−1) as a function of measured Dissolved Inorganc Carbon (DIC, µmol kg−1)
Differences vs Salinity
Figure Supp X: Linear regressions of (A) TA-n35TA and (B) DIC-nDIC as a function of Salinity
4. Endmember comparissions
FLK.data_filtered<-FLK.data[FLK.data$Extreme=="Normal",]4.1 TA and DIC relation
No_ocean.data<-FLK.data[FLK.data$Zone!="Oceanic",]
TA_DIC<- ggplot(No_ocean.data, aes (DIC_umol_kg, TA_umol_kg)) +
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
scale_x_continuous(# limits = c(1600, 2600),
# expand = c(0, 0),
# breaks = seq(1500,2600,100),
expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
facet_grid(Sub_region~Zone)+ MyTheme+
theme(legend.position = "bottom")+
#Season_fill+ Season_colour+
guides(fill=guide_legend(override.aes=list(shape=21)))TA_DIC_Zone <- ddply (FLK.data[FLK.data$Extreme=="Normal", ], .(Zone),summarise,
TAmean = mean (TA_umol_kg, na.rm = T),
DICmean = mean (DIC_umol_kg, na.rm = T),
nTAmean = mean (n35TA, na.rm = T),
nDICmean = mean (n35DIC, na.rm = T),
TAsd = sd (TA_umol_kg, na.rm = T),
DICsd = sd (DIC_umol_kg, na.rm = T),
nTAsd = sd (n35TA, na.rm = T),
nDIsd = sd (n35DIC, na.rm = T))
TA_DIC_Ocean<-TA_DIC_Zone[TA_DIC_Zone$Zone=="Oceanic", ]
kable(as.data.frame(TA_DIC_Zone, format = "markdown"),
caption = "Mean TA/DIC by zone") %>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | TAmean | DICmean | nTAmean | nDICmean | TAsd | DICsd | nTAsd | nDIsd |
|---|---|---|---|---|---|---|---|---|
| Inshore | 2340.787 | 2015.064 | 2333.990 | 2010.188 | 97.58327 | 110.70733 | 134.66366 | 147.79067 |
| Mid channel | 2364.723 | 2032.800 | 2359.007 | 2028.192 | 52.41657 | 58.19787 | 65.92103 | 74.37988 |
| Offshore | 2376.329 | 2043.844 | 2372.737 | 2040.658 | 19.67731 | 23.76709 | 37.86618 | 36.47511 |
| Oceanic | 2380.540 | 2044.319 | 2378.570 | 2042.690 | 11.82213 | 17.82925 | 34.44739 | 30.81862 |
Note: Use Model II Linear regressions!
“For Model II regressions, neither X nor Y is an INDEPENDENT variable but both are assumed to be DEPENDENT on some other parameter which is often unknown. Neither are”controlled”, both are measured, and both include some error. We do not seek an equation of how Y varies in response to a change in X, but rather we look for how they both co-vary in time or space in response to some other variable or process.”
Zone and region
# Individual LR for each Zone and Sub_region
TA_DIC_coef_zone_noExtremes <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_zone_noExtremes<-subset(TA_DIC_coef_zone_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_zone_noExtremes[,1:8],
format = "markdown"),
caption = "TA vs DIC equations by zone and region without extreme events",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | term | estimate | std.error | statistic | p.value | r.squared |
|---|---|---|---|---|---|---|---|
| Inshore | BB | DIC_umol_kg | 0.67 | 0.04 | 15.22 | 0 | 0.76 |
| Inshore | UK | DIC_umol_kg | 0.58 | 0.03 | 18.50 | 0 | 0.78 |
| Inshore | MK | DIC_umol_kg | 0.81 | 0.04 | 22.42 | 0 | 0.82 |
| Inshore | LK | DIC_umol_kg | 0.86 | 0.04 | 21.38 | 0 | 0.80 |
| Mid channel | BB | DIC_umol_kg | 0.57 | 0.06 | 9.71 | 0 | 0.58 |
| Mid channel | UK | DIC_umol_kg | 0.41 | 0.07 | 6.16 | 0 | 0.38 |
| Mid channel | MK | DIC_umol_kg | 0.83 | 0.04 | 21.55 | 0 | 0.81 |
| Mid channel | LK | DIC_umol_kg | 0.81 | 0.04 | 20.72 | 0 | 0.79 |
| Offshore | BB | DIC_umol_kg | 0.58 | 0.09 | 6.70 | 0 | 0.38 |
| Offshore | UK | DIC_umol_kg | 0.57 | 0.05 | 10.84 | 0 | 0.55 |
| Offshore | MK | DIC_umol_kg | 0.77 | 0.04 | 19.96 | 0 | 0.78 |
| Offshore | LK | DIC_umol_kg | 0.57 | 0.04 | 13.61 | 0 | 0.56 |
#write.csv(TA_DIC_coef_zone_noExtremes, "FLK_results/Fig_2_TA_DIC_coef_slopes_Subregion.csv", row.names = FALSE)
# Annotations for plots
coef_zone_noExtremes<-dplyr::select(TA_DIC_coef_zone_noExtremes,
c("Zone", "Sub_region", "estimate", "r.squared"))
coef_zone_noExtremes$estimate<-round(coef_zone_noExtremes$estimate, digits=2)
coef_zone_noExtremes$r.squared<-round(coef_zone_noExtremes$r.squared, digits=2)Type II Linear regression
TA_DIC_coef_zone_noExtII <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region) %>%
do({model = lmodel2(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
#TA_DIC_coef_zone_noExtII
TA_DIC_coef_zone_noExtII<-subset(TA_DIC_coef_zone_noExtII, term!="Intercept")
kable(as.data.frame(TA_DIC_coef_zone_noExtII[,1:9],
format = "markdown"),
caption = "TA vs DIC equations without extreme events using type II lm",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | method | term | estimate | conf.low | conf.high | p.value | r.squared |
|---|---|---|---|---|---|---|---|---|
| Inshore | BB | MA | Slope | 0.74 | 0.64 | 0.84 | 0.76 | |
| Inshore | BB | OLS | Slope | 0.67 | 0.58 | 0.76 | 0.76 | |
| Inshore | BB | SMA | Slope | 0.77 | 0.68 | 0.86 | 0.76 | |
| Inshore | UK | MA | Slope | 0.62 | 0.56 | 0.69 | 0.78 | |
| Inshore | UK | OLS | Slope | 0.58 | 0.52 | 0.64 | 0.78 | |
| Inshore | UK | SMA | Slope | 0.66 | 0.60 | 0.72 | 0.78 | |
| Inshore | MK | MA | Slope | 0.89 | 0.81 | 0.97 | 0.82 | |
| Inshore | MK | OLS | Slope | 0.81 | 0.74 | 0.88 | 0.82 | |
| Inshore | MK | SMA | Slope | 0.90 | 0.83 | 0.97 | 0.82 | |
| Inshore | LK | MA | Slope | 0.96 | 0.87 | 1.05 | 0.80 | |
| Inshore | LK | OLS | Slope | 0.86 | 0.78 | 0.94 | 0.80 | |
| Inshore | LK | SMA | Slope | 0.96 | 0.89 | 1.05 | 0.80 | |
| Mid channel | BB | MA | Slope | 0.69 | 0.55 | 0.84 | 0.58 | |
| Mid channel | BB | OLS | Slope | 0.57 | 0.45 | 0.69 | 0.58 | |
| Mid channel | BB | SMA | Slope | 0.75 | 0.64 | 0.87 | 0.58 | |
| Mid channel | UK | MA | Slope | 0.54 | 0.37 | 0.73 | 0.38 | |
| Mid channel | UK | OLS | Slope | 0.41 | 0.28 | 0.55 | 0.38 | |
| Mid channel | UK | SMA | Slope | 0.67 | 0.55 | 0.82 | 0.38 | |
| Mid channel | MK | MA | Slope | 0.91 | 0.83 | 1.00 | 0.81 | |
| Mid channel | MK | OLS | Slope | 0.83 | 0.75 | 0.90 | 0.81 | |
| Mid channel | MK | SMA | Slope | 0.92 | 0.85 | 1.00 | 0.81 | |
| Mid channel | LK | MA | Slope | 0.90 | 0.82 | 0.99 | 0.79 | |
| Mid channel | LK | OLS | Slope | 0.81 | 0.73 | 0.88 | 0.79 | |
| Mid channel | LK | SMA | Slope | 0.91 | 0.84 | 0.99 | 0.79 | |
| Offshore | BB | MA | Slope | 0.89 | 0.65 | 1.20 | 0.38 | |
| Offshore | BB | OLS | Slope | 0.58 | 0.40 | 0.75 | 0.38 | |
| Offshore | BB | SMA | Slope | 0.93 | 0.77 | 1.12 | 0.38 | |
| Offshore | UK | MA | Slope | 0.70 | 0.58 | 0.84 | 0.55 | |
| Offshore | UK | OLS | Slope | 0.57 | 0.47 | 0.67 | 0.55 | |
| Offshore | UK | SMA | Slope | 0.77 | 0.67 | 0.88 | 0.55 | |
| Offshore | MK | MA | Slope | 0.86 | 0.78 | 0.95 | 0.78 | |
| Offshore | MK | OLS | Slope | 0.77 | 0.70 | 0.85 | 0.78 | |
| Offshore | MK | SMA | Slope | 0.87 | 0.80 | 0.95 | 0.78 | |
| Offshore | LK | MA | Slope | 0.70 | 0.60 | 0.80 | 0.56 | |
| Offshore | LK | OLS | Slope | 0.57 | 0.49 | 0.65 | 0.56 | |
| Offshore | LK | SMA | Slope | 0.76 | 0.68 | 0.85 | 0.56 |
# Annotations for plots
TA_DIC_coef_zone_noExtII<-dplyr::select(TA_DIC_coef_zone_noExtII,
c("Zone", "Sub_region","method", "estimate", "r.squared"))
TA_DIC_coef_zone_noExtII$estimate<-round(TA_DIC_coef_zone_noExtII$estimate, digits=2)
TA_DIC_coef_zone_noExtII$r.squared<-round(TA_DIC_coef_zone_noExtII$r.squared, digits=2)4.2 By Precipitation - Removing Extreme events
TA_DIC_Extreme_pre<-TA_DIC +
geom_abline(intercept = -1700, slope=2, color="grey")+
geom_hline (yintercept = TA_DIC_Ocean$TAmean, color="grey")+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
geom_point(data=No_ocean.data, aes (DIC_umol_kg, TA_umol_kg,
fill=Precipitation, shape=Extreme),
alpha=0.5, size=1)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg),
method = "lm", colour="black", se=F)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg, colour=Precipitation),
method = "lm", se=F, size=0.5)+
geom_text(data = coef_zone_noExtremes,
aes(x = TA_DIC_Ocean$DICmean, y = 2070,
label = paste("Slo =", estimate,
" r.sq=", r.squared)),
size=2.5)+
# geom_abline(slope = 0, intercept =TA_DIC_Ocean$TAmean,
# linetype=2, colour="gray")+
# geom_vline(xintercept = TA_DIC_Ocean$DICmean,
# linetype=2, colour="gray")+
geom_point(aes(x=TA_DIC_Ocean$DICmean, y=TA_DIC_Ocean$TAmean),
size=3, alpha=1, shape=21, fill="black")
#TA_DIC_Extreme_preZone, region and transect
TA_DIC_reef_pre<-TA_DIC_Extreme_pre +
facet_grid(Zone~Reference)
#TA_DIC_reef_pre# Individual LR for each reef
TA_DIC_coef_Reef_noExtremes <- No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region, Reference, Precipitation) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_Reef_noExtremes<-subset(TA_DIC_coef_Reef_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_Reef_noExtremes[,1:11],
format = "markdown"),
caption = "TA vs DIC equations by reef without extreme events per trsansec",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | Reference | Precipitation | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared |
|---|---|---|---|---|---|---|---|---|---|---|
| Inshore | BB | BB_1 | Dry | DIC_umol_kg | 0.58 | 0.05 | 10.57 | 0.00 | 0.85 | 0.85 |
| Inshore | BB | BB_1 | Wet | DIC_umol_kg | 0.72 | 0.07 | 10.43 | 0.00 | 0.86 | 0.86 |
| Inshore | BB | BB_2 | Dry | DIC_umol_kg | 0.54 | 0.07 | 7.89 | 0.00 | 0.79 | 0.77 |
| Inshore | BB | BB_2 | Wet | DIC_umol_kg | 0.47 | 0.15 | 3.15 | 0.01 | 0.42 | 0.37 |
| Inshore | UK | UK_3 | Dry | DIC_umol_kg | 0.58 | 0.03 | 18.79 | 0.00 | 0.92 | 0.91 |
| Inshore | UK | UK_3 | Wet | DIC_umol_kg | 0.47 | 0.09 | 5.16 | 0.00 | 0.50 | 0.48 |
| Inshore | UK | UK_4 | Dry | DIC_umol_kg | 0.54 | 0.05 | 11.46 | 0.00 | 0.90 | 0.89 |
| Inshore | UK | UK_4 | Wet | DIC_umol_kg | 0.35 | 0.22 | 1.59 | 0.13 | 0.15 | 0.09 |
| Inshore | MK | MK_5 | Dry | DIC_umol_kg | 0.56 | 0.06 | 8.81 | 0.00 | 0.82 | 0.81 |
| Inshore | MK | MK_5 | Wet | DIC_umol_kg | 0.70 | 0.11 | 6.19 | 0.00 | 0.71 | 0.69 |
| Inshore | MK | MK_6 | Dry | DIC_umol_kg | 0.72 | 0.09 | 8.38 | 0.00 | 0.81 | 0.80 |
| Inshore | MK | MK_6 | Wet | DIC_umol_kg | 0.80 | 0.13 | 6.25 | 0.00 | 0.70 | 0.68 |
| Inshore | MK | MK_7 | Dry | DIC_umol_kg | 0.67 | 0.08 | 8.84 | 0.00 | 0.84 | 0.83 |
| Inshore | MK | MK_7 | Wet | DIC_umol_kg | 0.93 | 0.15 | 6.18 | 0.00 | 0.69 | 0.67 |
| Inshore | LK | LK_8 | Dry | DIC_umol_kg | 0.79 | 0.10 | 8.00 | 0.00 | 0.78 | 0.77 |
| Inshore | LK | LK_8 | Wet | DIC_umol_kg | 0.80 | 0.11 | 7.18 | 0.00 | 0.76 | 0.75 |
| Inshore | LK | LK_9 | Dry | DIC_umol_kg | 0.68 | 0.09 | 7.62 | 0.00 | 0.77 | 0.76 |
| Inshore | LK | LK_9 | Wet | DIC_umol_kg | 0.70 | 0.19 | 3.76 | 0.00 | 0.47 | 0.44 |
| Inshore | LK | LK_10 | Dry | DIC_umol_kg | 0.84 | 0.14 | 5.87 | 0.00 | 0.63 | 0.61 |
| Inshore | LK | LK_10 | Wet | DIC_umol_kg | 0.64 | 0.14 | 4.74 | 0.00 | 0.57 | 0.54 |
| Mid channel | BB | BB_1 | Dry | DIC_umol_kg | 0.53 | 0.08 | 6.67 | 0.00 | 0.72 | 0.71 |
| Mid channel | BB | BB_1 | Wet | DIC_umol_kg | 0.60 | 0.14 | 4.36 | 0.00 | 0.56 | 0.53 |
| Mid channel | BB | BB_2 | Dry | DIC_umol_kg | 0.52 | 0.09 | 5.54 | 0.00 | 0.66 | 0.64 |
| Mid channel | BB | BB_2 | Wet | DIC_umol_kg | 0.38 | 0.29 | 1.30 | 0.22 | 0.12 | 0.05 |
| Mid channel | UK | UK_3 | Dry | DIC_umol_kg | 0.36 | 0.14 | 2.58 | 0.02 | 0.36 | 0.30 |
| Mid channel | UK | UK_3 | Wet | DIC_umol_kg | 0.00 | 0.17 | -0.02 | 0.98 | 0.00 | -0.07 |
| Mid channel | UK | UK_4 | Dry | DIC_umol_kg | 0.49 | 0.10 | 4.77 | 0.00 | 0.60 | 0.58 |
| Mid channel | UK | UK_4 | Wet | DIC_umol_kg | 0.63 | 0.25 | 2.57 | 0.02 | 0.32 | 0.27 |
| Mid channel | MK | MK_5 | Dry | DIC_umol_kg | 0.47 | 0.10 | 4.53 | 0.00 | 0.53 | 0.51 |
| Mid channel | MK | MK_5 | Wet | DIC_umol_kg | 0.88 | 0.17 | 5.18 | 0.00 | 0.61 | 0.59 |
| Mid channel | MK | MK_6 | Dry | DIC_umol_kg | 0.59 | 0.09 | 6.35 | 0.00 | 0.70 | 0.69 |
| Mid channel | MK | MK_6 | Wet | DIC_umol_kg | 0.83 | 0.10 | 8.45 | 0.00 | 0.82 | 0.81 |
| Mid channel | MK | MK_7 | Dry | DIC_umol_kg | 0.63 | 0.07 | 8.50 | 0.00 | 0.82 | 0.81 |
| Mid channel | MK | MK_7 | Wet | DIC_umol_kg | 1.09 | 0.12 | 9.25 | 0.00 | 0.84 | 0.83 |
| Mid channel | LK | LK_8 | Dry | DIC_umol_kg | 0.56 | 0.16 | 3.50 | 0.00 | 0.39 | 0.36 |
| Mid channel | LK | LK_8 | Wet | DIC_umol_kg | 0.96 | 0.07 | 14.05 | 0.00 | 0.92 | 0.92 |
| Mid channel | LK | LK_9 | Dry | DIC_umol_kg | 0.67 | 0.10 | 6.51 | 0.00 | 0.70 | 0.69 |
| Mid channel | LK | LK_9 | Wet | DIC_umol_kg | 0.92 | 0.12 | 7.51 | 0.00 | 0.77 | 0.75 |
| Mid channel | LK | LK_10 | Dry | DIC_umol_kg | 0.42 | 0.18 | 2.37 | 0.03 | 0.22 | 0.18 |
| Mid channel | LK | LK_10 | Wet | DIC_umol_kg | 0.88 | 0.13 | 6.51 | 0.00 | 0.73 | 0.71 |
| Offshore | BB | BB_1 | Dry | DIC_umol_kg | 0.53 | 0.11 | 4.70 | 0.00 | 0.54 | 0.51 |
| Offshore | BB | BB_1 | Wet | DIC_umol_kg | 0.61 | 0.22 | 2.82 | 0.01 | 0.32 | 0.28 |
| Offshore | BB | BB_2 | Dry | DIC_umol_kg | 0.49 | 0.15 | 3.28 | 0.00 | 0.40 | 0.37 |
| Offshore | BB | BB_2 | Wet | DIC_umol_kg | 0.57 | 0.31 | 1.82 | 0.09 | 0.19 | 0.13 |
| Offshore | UK | UK_3 | Dry | DIC_umol_kg | 0.63 | 0.10 | 6.55 | 0.00 | 0.60 | 0.58 |
| Offshore | UK | UK_3 | Wet | DIC_umol_kg | 0.56 | 0.15 | 3.82 | 0.00 | 0.32 | 0.30 |
| Offshore | UK | UK_4 | Dry | DIC_umol_kg | 0.48 | 0.09 | 5.19 | 0.00 | 0.63 | 0.60 |
| Offshore | UK | UK_4 | Wet | DIC_umol_kg | 0.90 | 0.13 | 6.90 | 0.00 | 0.77 | 0.76 |
| Offshore | MK | MK_5 | Dry | DIC_umol_kg | 0.50 | 0.10 | 5.09 | 0.00 | 0.59 | 0.57 |
| Offshore | MK | MK_5 | Wet | DIC_umol_kg | 0.85 | 0.07 | 12.02 | 0.00 | 0.89 | 0.89 |
| Offshore | MK | MK_6 | Dry | DIC_umol_kg | 0.51 | 0.10 | 4.89 | 0.00 | 0.56 | 0.53 |
| Offshore | MK | MK_6 | Wet | DIC_umol_kg | 1.07 | 0.11 | 9.62 | 0.00 | 0.86 | 0.85 |
| Offshore | MK | MK_7 | Dry | DIC_umol_kg | 0.43 | 0.09 | 4.67 | 0.00 | 0.58 | 0.55 |
| Offshore | MK | MK_7 | Wet | DIC_umol_kg | 0.95 | 0.10 | 9.22 | 0.00 | 0.84 | 0.83 |
| Offshore | LK | LK_8 | Dry | DIC_umol_kg | 0.25 | 0.08 | 3.09 | 0.01 | 0.35 | 0.31 |
| Offshore | LK | LK_8 | Wet | DIC_umol_kg | 0.97 | 0.11 | 8.46 | 0.00 | 0.80 | 0.79 |
| Offshore | LK | LK_9 | Dry | DIC_umol_kg | 0.55 | 0.09 | 6.41 | 0.00 | 0.73 | 0.71 |
| Offshore | LK | LK_9 | Wet | DIC_umol_kg | 0.83 | 0.12 | 6.98 | 0.00 | 0.75 | 0.74 |
| Offshore | LK | LK_10 | Dry | DIC_umol_kg | 0.48 | 0.10 | 4.91 | 0.00 | 0.41 | 0.40 |
| Offshore | LK | LK_10 | Wet | DIC_umol_kg | 0.77 | 0.13 | 5.72 | 0.00 | 0.50 | 0.48 |
#write.csv (TA_DIC_coef_Reef_noExtremes, "FLK_results/Fig_2_TA_DIC_slopes_Reefs.csv", row.names = FALSE)
TA_DIC_coef_Reef_noExtremes <-
No_ocean.data[No_ocean.data$Extreme=="Normal", ] %>%
group_by(Zone, Region, Sub_region, Transect, Precipitation) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model infoSlopes_reef<-subset(TA_DIC_coef_Reef_noExtremes, term!="(Intercept)")
Slope_plot<-ggplot(data=Slopes_reef, aes (x =Zone, y =estimate)) +
MyTheme+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
geom_boxplot(aes(fill=(Precipitation)), alpha=0.5,
position=position_dodge(width=0.3))+
geom_point(aes(fill = factor(Precipitation)),
position=position_dodge(width=0.3),
shape = 21, size=0.5)+
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA vs DIC slope")))+
theme(axis.text.x = element_text(angle = 0, vjust = 0.1),
legend.position = "bottom")+
#guides(fill=FALSE)+
facet_grid(Sub_region~Region)
# Season_fill+ Season_colour+
# theme(legend.title = element_blank())
#Slope_plot
Figure S9: TA (µmol kg−1) vs DIC (µmol kg−1) slopes per season and reef
4.3 By Season - Removing Extreme events
TA_DIC_season<-TA_DIC +
Season_colour+ Season_fill+
geom_abline(intercept = -1700, slope=2, color="grey")+
geom_hline (yintercept = TA_DIC_Ocean$TAmean, color="grey")+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
geom_point(data=No_ocean.data, aes (DIC_umol_kg, TA_umol_kg,
fill=Season, shape=Extreme),
alpha=0.5, size=1)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg),
method = "lm", colour="black", se=F)+
geom_smooth(data=No_ocean.data[No_ocean.data$Extreme=="Normal", ],
aes(DIC_umol_kg, TA_umol_kg, colour=Season),
method = "lm", se=F, size=0.5)+
geom_text(data = coef_zone_noExtremes,
aes(x = TA_DIC_Ocean$DICmean, y = 2070,
label = paste("Slo =", estimate,
" r.sq=", r.squared)),
size=2.5)+
geom_point(aes(x=TA_DIC_Ocean$DICmean, y=TA_DIC_Ocean$TAmean),
size=3, alpha=1, shape=21, fill="black")
#TA_DIC_season# Individual LR for each reef
TA_DIC_coef_Season_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>%
group_by(Zone, Sub_region, Season) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model info
TA_DIC_coef_Season_noExtremes<-subset(TA_DIC_coef_Season_noExtremes, term!="(Intercept)")
kable(as.data.frame(TA_DIC_coef_Season_noExtremes[,1:11],
format = "markdown"),
caption = "TA vs DIC equations by season without extreme events",
digits = 2)%>%
kable_styling(bootstrap_options = c("striped", "condensed", full_width = F), font_size = 11)| Zone | Sub_region | Season | term | estimate | std.error | statistic | p.value | r.squared | adj.r.squared | sigma |
|---|---|---|---|---|---|---|---|---|---|---|
| Inshore | BB | Winter | DIC_umol_kg | 0.67 | 0.10 | 6.59 | 0.00 | 0.74 | 0.73 | 23.70 |
| Inshore | BB | Spring | DIC_umol_kg | 0.86 | 0.11 | 7.52 | 0.00 | 0.75 | 0.74 | 36.03 |
| Inshore | BB | Summer | DIC_umol_kg | 0.54 | 0.15 | 3.61 | 0.00 | 0.48 | 0.45 | 37.42 |
| Inshore | BB | Fall | DIC_umol_kg | 0.80 | 0.09 | 8.72 | 0.00 | 0.80 | 0.79 | 22.56 |
| Inshore | UK | Winter | DIC_umol_kg | 0.71 | 0.05 | 13.83 | 0.00 | 0.91 | 0.90 | 21.12 |
| Inshore | UK | Spring | DIC_umol_kg | 0.58 | 0.06 | 8.96 | 0.00 | 0.76 | 0.75 | 22.93 |
| Inshore | UK | Summer | DIC_umol_kg | 0.80 | 0.27 | 2.92 | 0.01 | 0.28 | 0.25 | 42.64 |
| Inshore | UK | Fall | DIC_umol_kg | 0.72 | 0.06 | 11.23 | 0.00 | 0.85 | 0.84 | 27.46 |
| Inshore | MK | Winter | DIC_umol_kg | 0.84 | 0.08 | 10.16 | 0.00 | 0.84 | 0.83 | 34.06 |
| Inshore | MK | Spring | DIC_umol_kg | 0.84 | 0.10 | 8.32 | 0.00 | 0.68 | 0.67 | 41.83 |
| Inshore | MK | Summer | DIC_umol_kg | 1.08 | 0.10 | 11.08 | 0.00 | 0.85 | 0.85 | 37.41 |
| Inshore | MK | Fall | DIC_umol_kg | 0.82 | 0.07 | 11.50 | 0.00 | 0.83 | 0.82 | 27.51 |
| Inshore | LK | Winter | DIC_umol_kg | 0.64 | 0.12 | 5.22 | 0.00 | 0.51 | 0.49 | 44.09 |
| Inshore | LK | Spring | DIC_umol_kg | 0.87 | 0.08 | 10.52 | 0.00 | 0.77 | 0.76 | 35.49 |
| Inshore | LK | Summer | DIC_umol_kg | 0.90 | 0.12 | 7.58 | 0.00 | 0.76 | 0.75 | 39.84 |
| Inshore | LK | Fall | DIC_umol_kg | 1.08 | 0.06 | 17.96 | 0.00 | 0.91 | 0.91 | 25.26 |
| Mid channel | BB | Winter | DIC_umol_kg | 0.80 | 0.12 | 6.54 | 0.00 | 0.75 | 0.74 | 9.85 |
| Mid channel | BB | Spring | DIC_umol_kg | 0.64 | 0.08 | 7.88 | 0.00 | 0.80 | 0.78 | 11.95 |
| Mid channel | BB | Summer | DIC_umol_kg | 0.61 | 0.36 | 1.71 | 0.11 | 0.17 | 0.11 | 21.37 |
| Mid channel | BB | Fall | DIC_umol_kg | 0.56 | 0.11 | 5.32 | 0.00 | 0.63 | 0.60 | 11.63 |
| Mid channel | UK | Winter | DIC_umol_kg | 0.66 | 0.09 | 7.62 | 0.00 | 0.82 | 0.80 | 13.29 |
| Mid channel | UK | Spring | DIC_umol_kg | 0.01 | 0.13 | 0.04 | 0.97 | 0.00 | -0.07 | 13.59 |
| Mid channel | UK | Summer | DIC_umol_kg | 1.09 | 0.32 | 3.45 | 0.00 | 0.50 | 0.46 | 17.14 |
| Mid channel | UK | Fall | DIC_umol_kg | 0.57 | 0.13 | 4.44 | 0.00 | 0.57 | 0.54 | 14.12 |
| Mid channel | MK | Winter | DIC_umol_kg | 0.72 | 0.07 | 10.08 | 0.00 | 0.82 | 0.81 | 21.50 |
| Mid channel | MK | Spring | DIC_umol_kg | 0.97 | 0.08 | 11.47 | 0.00 | 0.81 | 0.80 | 29.81 |
| Mid channel | MK | Summer | DIC_umol_kg | 1.25 | 0.10 | 12.61 | 0.00 | 0.88 | 0.87 | 21.92 |
| Mid channel | MK | Fall | DIC_umol_kg | 0.63 | 0.06 | 10.51 | 0.00 | 0.80 | 0.79 | 14.44 |
| Mid channel | LK | Winter | DIC_umol_kg | 0.73 | 0.14 | 5.10 | 0.00 | 0.48 | 0.46 | 35.75 |
| Mid channel | LK | Spring | DIC_umol_kg | 0.74 | 0.08 | 9.40 | 0.00 | 0.73 | 0.73 | 27.01 |
| Mid channel | LK | Summer | DIC_umol_kg | 0.97 | 0.06 | 15.11 | 0.00 | 0.92 | 0.92 | 18.73 |
| Mid channel | LK | Fall | DIC_umol_kg | 0.82 | 0.16 | 4.96 | 0.00 | 0.44 | 0.42 | 19.50 |
| Offshore | BB | Winter | DIC_umol_kg | 0.74 | 0.12 | 5.98 | 0.00 | 0.70 | 0.68 | 8.49 |
| Offshore | BB | Spring | DIC_umol_kg | 0.48 | 0.13 | 3.63 | 0.00 | 0.41 | 0.38 | 8.38 |
| Offshore | BB | Summer | DIC_umol_kg | 0.84 | 0.37 | 2.27 | 0.04 | 0.27 | 0.22 | 21.24 |
| Offshore | BB | Fall | DIC_umol_kg | 0.32 | 0.11 | 2.93 | 0.01 | 0.32 | 0.29 | 8.97 |
| Offshore | UK | Winter | DIC_umol_kg | 0.63 | 0.08 | 8.01 | 0.00 | 0.75 | 0.74 | 6.58 |
| Offshore | UK | Spring | DIC_umol_kg | 0.48 | 0.17 | 2.86 | 0.01 | 0.25 | 0.22 | 10.70 |
| Offshore | UK | Summer | DIC_umol_kg | 0.93 | 0.14 | 6.78 | 0.00 | 0.70 | 0.68 | 9.77 |
| Offshore | UK | Fall | DIC_umol_kg | 0.48 | 0.11 | 4.36 | 0.00 | 0.44 | 0.42 | 10.84 |
| Offshore | MK | Winter | DIC_umol_kg | 0.62 | 0.09 | 6.95 | 0.00 | 0.68 | 0.66 | 8.56 |
| Offshore | MK | Spring | DIC_umol_kg | 0.89 | 0.07 | 12.65 | 0.00 | 0.84 | 0.83 | 13.80 |
| Offshore | MK | Summer | DIC_umol_kg | 0.99 | 0.08 | 11.80 | 0.00 | 0.86 | 0.86 | 12.32 |
| Offshore | MK | Fall | DIC_umol_kg | 0.43 | 0.09 | 5.03 | 0.00 | 0.47 | 0.45 | 9.97 |
| Offshore | LK | Winter | DIC_umol_kg | 0.48 | 0.09 | 5.28 | 0.00 | 0.47 | 0.45 | 10.09 |
| Offshore | LK | Spring | DIC_umol_kg | 0.61 | 0.08 | 7.90 | 0.00 | 0.61 | 0.60 | 11.79 |
| Offshore | LK | Summer | DIC_umol_kg | 0.89 | 0.14 | 6.41 | 0.00 | 0.60 | 0.59 | 12.52 |
| Offshore | LK | Fall | DIC_umol_kg | 0.30 | 0.08 | 3.52 | 0.00 | 0.24 | 0.22 | 7.99 |
| Oceanic | UK | Winter | DIC_umol_kg | 0.74 | 0.10 | 7.49 | 0.00 | 0.90 | 0.89 | 4.81 |
| Oceanic | UK | Spring | DIC_umol_kg | 0.17 | 0.21 | 0.82 | 0.44 | 0.09 | -0.04 | 5.84 |
| Oceanic | UK | Summer | DIC_umol_kg | 0.86 | 0.31 | 2.79 | 0.05 | 0.66 | 0.58 | 11.68 |
| Oceanic | UK | Fall | DIC_umol_kg | 0.33 | 0.22 | 1.51 | 0.18 | 0.28 | 0.16 | 10.94 |
| Oceanic | MK | Winter | DIC_umol_kg | 0.58 | 0.07 | 8.35 | 0.00 | 0.83 | 0.82 | 5.13 |
| Oceanic | MK | Spring | DIC_umol_kg | 0.45 | 0.12 | 3.77 | 0.00 | 0.47 | 0.44 | 8.16 |
| Oceanic | MK | Summer | DIC_umol_kg | 0.79 | 0.11 | 7.49 | 0.00 | 0.80 | 0.79 | 6.42 |
| Oceanic | MK | Fall | DIC_umol_kg | 0.32 | 0.12 | 2.60 | 0.02 | 0.27 | 0.23 | 10.20 |
| Oceanic | LK | Winter | DIC_umol_kg | 0.45 | 0.15 | 3.02 | 0.01 | 0.35 | 0.31 | 11.24 |
| Oceanic | LK | Spring | DIC_umol_kg | 0.51 | 0.11 | 4.73 | 0.00 | 0.53 | 0.50 | 8.38 |
| Oceanic | LK | Summer | DIC_umol_kg | 0.66 | 0.13 | 4.94 | 0.00 | 0.69 | 0.66 | 7.63 |
| Oceanic | LK | Fall | DIC_umol_kg | 0.27 | 0.12 | 2.18 | 0.04 | 0.22 | 0.17 | 7.33 |
#write.csv (TA_DIC_coef_Season_noExtremes, "FLK_results/Fig_5_TA_DIC_slopes_Reefs.csv", row.names = FALSE)
TA_DIC_coef_Season_noExtremes <- FLK.data[FLK.data$Extreme=="Normal", ] %>%
group_by(Zone, Region, Sub_region, Season, Transect) %>%
do({model = lm(TA_umol_kg~DIC_umol_kg, data=.)
# create model
data.frame(tidy(model), # get coefficient info
glance(model))}) # get model infoSlopes_season<-subset(TA_DIC_coef_Season_noExtremes, term!="(Intercept)")
Slope_plot<-ggplot(data=Slopes_season, aes (x =Zone, y =estimate)) +
MyTheme+
Zone_shapes4+
geom_boxplot(aes(fill=Season), alpha=0.5,
position=position_dodge(width=0.3))+
geom_point(aes(fill = factor(Season)),
position=position_dodge(width=0.3),
shape = 21, size=0.5)+
scale_y_continuous(#limits = c(1700,2900),
#expand = c(0, 0),
#breaks = seq(1800, 2800, 100),
expression(paste("TA vs DIC slope")))+
facet_grid(Sub_region~Region)+
Season_fill+
Season_colour+
guides(fill=FALSE)+
theme(axis.text.x = element_text(angle = 90, vjust = 0.1),
legend.position = "bottom")
#Slope_plot
Figure 6: TA (µmol kg−1) vs DIC (µmol kg−1) slopes per season and reef
5 Seasonal plots
Data for endmember comparisons
# Reef
WS.reef<-subset(FLK.data, FLK.data$Zone!="Oceanic")
WS.reef$Date<-as.Date(WS.reef$Date, "%Y-%m-%d")
# Open
WS.open<-subset(FLK.data_filtered,
FLK.data_filtered$Zone=="Oceanic")
WS.open$Date<-as.Date(WS.open$Date, "%Y-%m-%d")
#summary(WS.open)# Mean ocean values by month - year
mean_Open_MY<- ddply (WS.open, .(MY, Month),
summarise,
OTem_my = mean (Temperature_C, na.rm = T),
OSal_my = mean (BestSalinity, na.rm = T),
OTA_my = mean (TA_umol_kg, na.rm = T),
OnTA_my = mean (n35TA, na.rm = T),
ODIC_my = mean (DIC_umol_kg, na.rm = T),
OnDIC_my = mean (n35DIC, na.rm = T),
OpCO2_O = mean (pCO2_uatm , na.rm = T),
OpH_O = mean (pH_calculated, na.rm = T),
OAra_O = mean (Aragonite_Sat_W , na.rm = T)
)
# Mean ocean values by month
mean_OpM <- mean_Open_MY %>%
group_by(Month) %>%
summarise(
OTem_m = mean (OTem_my, na.rm = T),
OSal_m = mean (OSal_my, na.rm = T),
OTA_m = mean (OTA_my, na.rm = T),
OnTA_m = mean (OnTA_my, na.rm = T),
OnDIC_m = mean (OnDIC_my, na.rm = T),
ODIC_m = mean (ODIC_my, na.rm = T),
OpCO2_m = mean (OpCO2_O , na.rm = T),
OpH_m = mean (OpH_O, na.rm = T),
OAra_m = mean (OAra_O, na.rm = T),
n = n()
)
#mean_OpM
# Mean ocean values by year
mean_Op<- mean_OpM %>%
summarise(
OTem = mean (OTem_m, na.rm = T),
OSal = mean (OSal_m, na.rm = T),
OTA = mean (OTA_m, na.rm = T),
OnTA = mean (OnTA_m, na.rm = T),
OnDIC = mean (OnDIC_m, na.rm = T),
ODIC = mean (ODIC_m, na.rm = T),
OpCO2 = mean (OpCO2_m, na.rm = T),
OpH = mean (OpH_m, na.rm = T),
OAra = mean (OAra_m, na.rm = T),
n = n()
)5.1 Month raw values
- Months stats
Month_Stats<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region),
summarise,
Tem_mean = mean (Temperature_C, na.rm = T),
Tem_sd = sd (Temperature_C, na.rm = T),
#Tem_se = std.error (Temperature_C, na.rm = T),
Sal_mean = mean (BestSalinity, na.rm = T),
Sal_sd = sd (BestSalinity, na.rm = T),
#Sal_se = std.error (BestSalinity, na.rm = T),
TA_mean = mean (TA_umol_kg, na.rm = T),
TA_sd = sd (TA_umol_kg, na.rm = T),
#TA_se = std.error (TA_umol_kg, na.rm = T),
nTA_mean = mean (n35TA, na.rm = T),
nTA_sd = sd (n35TA, na.rm = T),
DIC_mean = mean (DIC_umol_kg, na.rm = T),
DIC_sd = sd (DIC_umol_kg, na.rm = T),
#DIC_se = std.error (DIC_umol_kg, na.rm = T),
nDIC_mean = mean (n35DIC, na.rm = T),
nDIC_sd = sd (n35DIC, na.rm = T),
pCO2_mean = mean (pCO2_uatm , na.rm = T),
pCO2_sd = sd (pCO2_uatm , na.rm = T),
#pCO2_se = std.error (pCO2_uatm, na.rm = T),
pH_mean = mean (pH_calculated, na.rm = T),
pH_sd = sd (pH_calculated, na.rm = T),
#pH_se = std.error (pH_calculated, na.rm = T),
Ara_mean = mean (Aragonite_Sat_W , na.rm = T),
Ara_sd = sd (Aragonite_Sat_W , na.rm = T),
#Ara_se = std.error (Aragonite_Sat_W, na.rm = T),
n = n())
Month_Stats_2d<-Month_Stats %>%
mutate_if(is.numeric, round, digits=3)
Month_Stats_2d#write.csv(Month_Stats, "FLK_results/Table_S7_Month_Stats.csv", row.names = F)Temp
Month_Temp<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (Temperature_C, na.rm = T),
sd = sd (Temperature_C, na.rm = T))
Month_Temp<-Month_Temp %>%
mutate_if(is.numeric, round, digits=2)
Month_Temp$Region<-NULL
Month_Temp<-reshape(Month_Temp, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")Sal
Month_Sal<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (BestSalinity, na.rm = T),
sd = sd (BestSalinity, na.rm = T))
Month_Sal<-Month_Sal %>%
mutate_if(is.numeric, round, digits=2)
Month_Sal$Region<-NULL
Month_Sal<-reshape(Month_Sal, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")TA
Month_TA<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (TA_umol_kg, na.rm = T),
sd = sd (TA_umol_kg, na.rm = T))
Month_TA<-Month_TA %>%
mutate_if(is.numeric, round, digits=2)
Month_TA$Region<-NULL
Month_TA<-reshape(Month_TA, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")DIC
Month_DIC<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (DIC_umol_kg, na.rm = T),
sd = sd (DIC_umol_kg, na.rm = T))
Month_DIC<-Month_DIC %>%
mutate_if(is.numeric, round, digits=2)
Month_DIC$Region<-NULL
Month_DIC<-reshape(Month_DIC, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")pCO2
Month_pCO2<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (pCO2_uatm , na.rm = T),
sd = sd (pCO2_uatm , na.rm = T))
Month_pCO2<-Month_pCO2 %>%
mutate_if(is.numeric, round, digits=2)
Month_pCO2$Region<-NULL
Month_pCO2<-reshape(Month_pCO2, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")pH
Month_pH<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (pH_calculated, na.rm = T),
sd = sd (pH_calculated, na.rm = T))
Month_pH<-Month_pH %>%
mutate_if(is.numeric, round, digits=2)
Month_pH$Region<-NULL
Month_pH<-reshape(Month_pH, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")Arag
Month_Ara<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season, Month2, Region), summarise,
mean = mean (Aragonite_Sat_W , na.rm = T),
sd = sd (Aragonite_Sat_W , na.rm = T))
Month_Ara<-Month_Ara %>%
mutate_if(is.numeric, round, digits=2)
Month_Ara$Region<-NULL
Month_Ara<-reshape(Month_Ara, idvar = c("Sub_region",
"Season", "Month2"),
timevar = "Zone", direction = "wide")all
Month_Stats3<-rbind(Month_Temp, Month_Sal, Month_TA, Month_DIC,
Month_pCO2, Month_pH, Month_Ara)
#write.csv(Month_Stats3, "FLK_results/Table_S7_Month_Stats_wide.csv", row.names = F)Seasonal range
Seasonal_range<-ddply (Month_Stats, .(Zone, Sub_region),
summarise,
Tem_meany = mean (Tem_mean, na.rm = T),
Tem_sdy = sd (Tem_mean, na.rm = T),
Tem_max = max (Tem_mean, na.rm = T),
Tem_min = min (Tem_mean, na.rm = T),
Tem_diff = (max (Tem_mean, na.rm = T) - min (Tem_mean, na.rm = T)),
Sal_meany = mean (Sal_mean, na.rm = T),
Sal_sdy = sd (Sal_mean, na.rm = T),
Sal_max = max (Sal_mean, na.rm = T),
Sal_min = min (Sal_mean, na.rm = T),
Sal_diff = (max (Sal_mean, na.rm = T) - min (Sal_mean, na.rm = T)),
TA_meany = mean (TA_mean, na.rm = T),
TA_sdy = sd (TA_mean, na.rm = T),
TA_max = max (TA_mean, na.rm = T),
TA_min = min (TA_mean, na.rm = T),
TA_diff = (max (TA_mean, na.rm = T) - min (TA_mean, na.rm = T)),
DIC_meany = mean (DIC_mean, na.rm = T),
DIC_sdy = sd (DIC_mean, na.rm = T),
DIC_max = max (DIC_mean, na.rm = T),
DIC_min = min (DIC_mean, na.rm = T),
DIC_diff = (max (DIC_mean, na.rm = T) - min (DIC_mean, na.rm = T)),
pCO2_meany = mean (pCO2_mean, na.rm = T),
pCO2_sdy = sd (pCO2_mean, na.rm = T),
pCO2_max = max (pCO2_mean, na.rm = T),
pCO2_min = min (pCO2_mean, na.rm = T),
pCO2_dif = (max (pCO2_mean, na.rm = T) - min (pCO2_mean, na.rm = T)),
pH_meany = mean (pH_mean, na.rm = T),
pH_sdy = sd (pH_mean, na.rm = T),
pH_max = max (pH_mean, na.rm = T),
pH_min = min (pH_mean, na.rm = T),
pH_diff = (max (pH_mean, na.rm = T) - min (pH_mean, na.rm = T)),
Arg_meany = mean (Ara_mean, na.rm = T),
Arg_sdy = sd (Ara_mean, na.rm = T),
Ara_max = max (Ara_mean, na.rm = T),
Ara_min = min (Ara_mean, na.rm = T),
Ara_diff = (max (Ara_mean, na.rm = T) - min (Ara_mean, na.rm = T)),
nTA_max = max (nTA_mean, na.rm = T),
nTA_min = min (nTA_mean, na.rm = T),
nTA_diff = (max (nTA_mean, na.rm = T) - min (nTA_mean, na.rm = T)),
nDIC_max = max (nDIC_mean, na.rm = T),
nDIC_min = min (nDIC_mean, na.rm = T),
nDIC_diff = (max (nDIC_mean, na.rm = T) - min (nDIC_mean, na.rm = T)),
n = n())
Seasonal_range<-Seasonal_range %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_range#Seasonal_range$pH_max_diff<-Seasonal_range$pH_max-mean()
#write.csv(Seasonal_range, "FLK_results/Table_S8_Month_mean_range.csv", row.names = F)- Month Plots
Month_ploth<- ggplot(FLK.data) +
MyTheme +
facet_grid(Sub_region~Zone)+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank() )
Year_ploth<- ggplot(Month_Stats) +
MyTheme +
theme(axis.title.x = element_blank(), axis.text.x=element_text(angle = 10, vjust = 0.5))+
Season_fill+
Season_colour+
facet_grid(Sub_region~Region)
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
#theme(axis.title.x = element_blank() )
Month_Season_plotTA_DIC<- ggplot(FLK.data_filtered[
FLK.data_filtered$Zone!="Oceanic",]) +
MyTheme +
facet_grid(~Zone)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(#strip.text = element_blank(),
legend.position = "none",
axis.title.x = element_blank()
)
Month_Season_plot<- ggplot(FLK.data_filtered) +
MyTheme +
facet_grid(~Zone)+
Zone_shapes4+
#Region_fill+
#Region_colour+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(axis.title.x = element_blank())#,
# legend.position = "none")
Year_plot_2<- ggplot(Month_Stats)+
#[Month_Stats$Zone!="Oceanic",]) +
MyTheme +
facet_grid(~Zone)+
Zone_shapes4+
guides(fill=guide_legend(override.aes=list(shape=21)))+
#Season_fill+
#Season_colour+
theme(#axis.title.x = element_blank()#,
#axis.text.y=element_blank(),
legend.position = "bottom"
)
Year_plot_3<- ggplot(Month_Stats, aes(fill=Sub_region, shape=Zone))+
#[Month_Stats$Zone!="Oceanic",]) +
MyTheme +
Zone_shapes4+
facet_grid(~Region)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(#axis.title.x = element_blank()#,
#axis.text.y=element_blank(),
legend.position = "right",
axis.title.x = element_blank(),
axis.text.x = element_text(angle = 20, vjust = 0.5)
)Temperature
Figure Supp 2: Temperature variation by the region and shelf position by (A) month of the year and (B) season.
Temp_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OTem, color="grey")+
scale_y_continuous(#limits = c(15,40),
name=(expression("Temperature"~(degree*C))),
breaks = seq(16, 40, 2),
expand = c(0.05, 0.05)) +
geom_smooth(aes (x=as.numeric(Month2),
y=Temperature_C, colour=Sub_region),
span=0.6, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=Temperature_C, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=Temperature_C, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Temp_expSalinity
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 19.37 23.40 24.63 24.53 25.86 27.96
Figure Supp 3: Salinity variation by the region and shelf position by (A) month of the year and (B) season.
Sal_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OSal, color="grey")+
scale_y_continuous(#limits = c(15,40),
name=(expression("Salinity")),
breaks = seq(15, 40, 1),
expand = c(0.05, 0.05)) +
geom_smooth(aes (x=as.numeric(Month2),
y=BestSalinity, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=BestSalinity, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=BestSalinity, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Sal_expTA
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2045 2365 2464 2455 2561 2728
Figure Supp XX: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3A
TA_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(#limits = c(2100,2500),
expand = c(0.05, 0.05),
breaks = seq(2100, 2500, 100),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
# geom_smooth(data=FLK.data, se=F, colour="gray70", alpha=0.5, size=0.5,
# aes(x=as.numeric(Month2), y=Temp.adj))+
geom_smooth(aes (x=as.numeric(Month2),
y=TA_umol_kg, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=TA_umol_kg, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=TA_umol_kg, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#
TA_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(#limits = c(2150,2550),
expand = c(0.05, 0.05),
breaks = seq(2200, 2500, 50),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
stat_summary(aes (x=Sub_region, y=TA_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2)+
stat_summary(aes (x=Sub_region, y=TA_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#TA_Y2
TA_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OTA, color="grey")+
scale_y_continuous(limits = c(2130,2510),
expand = c(0.05, 0.05),
breaks = seq(2100, 2500, 100),
name=expression(paste("TA (", mu, "mol ", kg^-1, ")")))+
geom_point(data=Seasonal_range, aes (x=Zone, y=TA_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=TA_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = TA_min,
# xend = Zone, yend = TA_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=TA_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=TA_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#TA_Y3
Figure 3A: Total Alkalinity (TA) variation by the region and shelf position. Left: monthly mean values, Right: Yearly mean values
DIC
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 1702 2003 2096 2088 2188 2345
Figure Supp XX: Disolved Inorganic Carbon (DIC) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3B
DIC_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(#limits = c(1780,2270),
expand = c(0.05, 0.05),
breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=DIC_umol_kg, colour=Sub_region),
span=0.8, alpha=0.2, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=DIC_umol_kg, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width=0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=DIC_umol_kg, fill=Sub_region, shape=Zone),
size=1,
fun = "mean", geom = "point",
position=position_dodge(width=0.3))
#DIC_exp
DIC_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
#breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
stat_summary(aes (x=Sub_region, y=DIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)+
stat_summary(aes (x=Sub_region, y=DIC_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#DIC_Y2
DIC_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$ODIC, color="grey")+
scale_y_continuous(limits = c(1820,2230),
expand = c(0.00, 0.00),
breaks = seq(1800, 2800, 100),
name=expression(paste("DIC (", mu, "mol ", kg^-1, ")")))+
geom_point(data=Seasonal_range, aes (x=Zone, y=DIC_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=DIC_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = DIC_min,
# xend = Zone, yend = DIC_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=DIC_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=DIC_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#DIC_Y3
Figure 3B: Dissolved Inorganic Carbon (DIC) by the region and shelf position. Left: monthly mean values, Right: Yearly mean values
pCO2
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 208.0 466.4 545.9 539.0 624.7 759.2
Figure Supp 6: pCO2 variation by the region and shelf position by (A) month of the year and (B) season.
pCO2_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OpCO2, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=expression(paste(pCO[2], " (", mu, " atm)")),
breaks = seq(200, 600, 100),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=pCO2_uatm, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=pCO2_uatm, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=pCO2_uatm, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pCO2_exp
pCO2_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OpCO2, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=expression(paste(pCO[2],
" (", mu, " atm)")),
breaks = seq(200, 600, 50),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=pCO2_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)+
stat_summary(aes (x=Sub_region, y=pCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)
#pCO2_Y2
pH
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 7.808 8.027 8.094 8.088 8.160 8.274
Figure Supp 7: pH variation by the region and shelf position by (A) month of the year and (B) season.
pH_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(#limits = c(7.88,8.25),
name=(expression (pH)),
breaks = seq(0, 9, 0.1),
expand = c(0.02, 0)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=pH_calculated, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=pH_calculated, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=pH_calculated, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pH_exp
pH_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=(expression (pH[calculated])),
breaks = seq(0, 9, 0.05),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=pH_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)+
stat_summary(aes (x=Sub_region, y=pH_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)
#pH_Y2
pH_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OpH, color="grey")+
scale_y_continuous(limits = c(7.87,8.22),
name=(expression (pH)),
breaks = seq(0, 9, 0.1),
expand = c(0.02, 0)
)+
geom_point(data=Seasonal_range, aes (x=Zone, y=pH_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=pH_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = pH_min,
# xend = Zone, yend = pH_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=pH_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=pH_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#pH_Y3Figure 3C
Omega
## Min. 1st Qu. Median Mean 3rd Qu. Max.
## 2.246 3.597 4.013 3.977 4.425 5.129
Figure Supp 8: Omega variation by the region and shelf position by (A) month of the year and (B) season.
Figure 3D
Omega_exp<- Month_Season_plot+
geom_abline(slope = 0, intercept = mean_Op$OAra, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
name=(expression("\n" ~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, colour=Sub_region),
span=0.8, alpha=0.2, se=F
#method = lm, formula = y ~ splines::bs(x, 3),
)+
stat_summary(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.3))+
stat_summary(aes (x=as.numeric(Month2),
y=Aragonite_Sat_W, fill=Sub_region, shape=Zone),
size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.3))
#Omega_exp
Om_Y2<- Year_plot_2+
geom_abline(slope = 0, intercept = mean_Op$OAra, color="grey")+
scale_y_continuous(#limits = c(1,5.5),
# name=(expression (
# "Aragonite saturation state "(~Omega[Ar]))),
name=(expression (
~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.05, 0.05)
) +
stat_summary(aes (x=Sub_region, y=Ara_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar", width = 0.2)+
stat_summary(aes (x=Sub_region, y=Ara_mean, fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=2)
#Om_Y2
Om_Y3<- Year_plot_3+
geom_abline(slope = 0, intercept = mean_Op$OAra , color="grey")+
scale_y_continuous(#limits = c(1,5.5),
# name=(expression (
# "Aragonite saturation state "(~Omega[Ar]))),
name=(expression (
~Omega[Ar])),
breaks = seq(1, 5, 0.5),
expand = c(0.1, 0.1)
) +
geom_point(data=Seasonal_range, aes (x=Zone, y=Ara_max,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
geom_point(data=Seasonal_range, aes (x=Zone, y=Ara_min,
shape=Zone, colour=Sub_region),
alpha=0.8, size=1,
position = position_dodge(width = 0.6))+
# geom_segment(data=Seasonal_range,
# aes( x = Zone, y = Ara_min,
# xend = Zone, yend = Ara_max,
# colour=Sub_region),
# #position = position_dodge(width = 0.6),
# alpha=0.3)+
stat_summary(aes (x=Zone, y=Ara_mean, colour=Sub_region),
fun.data = "mean_sdl", fun.args = list(mult = 1),
geom = "errorbar", width = 0.2,
position = position_dodge(width = 0.6))+
stat_summary(aes (x=Zone, y=Ara_mean, fill=Sub_region, shape=Zone),
fun = "mean",
geom = "point", size=2,
position = position_dodge(width = 0.6))
#Om_Y3
Figure 3C: Aragonite saturation state (omega) by the region and shelf position. Left: monthly mean values, Right: Yearly mean values + SD, maximum monthly mean and minimum monthly mean.
Fig.3: Seasonal monthly plot
No summary
with summary
Fig.3: Seasonal monthly plot supplemental
Figure 3: Monthly seasonal patterns in (A) Total Alkalinity (TA), (B) Dissolved Inorganic Carbon (DIC), and (C) aragonite saturation state (Ωar) in the Florida Reef Tract. The colors represent the biogeographical regions and the vertical panels separate the locations by shelf zone. The points show the (monthly mean values ± 95 CI) and the lines the smoothed curves for the data (method = ‘loess’ and formula ‘y ~ x’, span=0.8). Gray horizontal lines in each panel demarcate the mean values of the parameters in the oceanic samples.
5.2 Season raw values
- Season stats (all data)
- do not use for further calculations since some months have more data than
- can be used to compare the aggregated meas ussing MM
Seasonal_Stats<- ddply (FLK.data_filtered, .(Zone, Sub_region, Season),
summarise,
Tem_mean = mean (Temperature_C, na.rm = T),
Tem_sd = sd (Temperature_C, na.rm = T),
Sal_mean = mean (BestSalinity, na.rm = T),
Sal_sd = sd (BestSalinity, na.rm = T),
TA_mean = mean (TA_umol_kg, na.rm = T),
TA_sd = sd (TA_umol_kg, na.rm = T),
nTA_mean = mean (n35TA, na.rm = T),
nTA_sd = sd (n35TA, na.rm = T),
nDIC_mean = mean (n35DIC, na.rm = T),
nDIC_sd = sd (n35DIC, na.rm = T),
DIC_mean = mean (DIC_umol_kg, na.rm = T),
DIC_sd = sd (DIC_umol_kg, na.rm = T),
pCO2_mean = mean (pCO2_uatm , na.rm = T),
pCO2_sd = sd (pCO2_uatm , na.rm = T),
pH_mean = mean (pH_calculated, na.rm = T),
pH_sd = sd (pH_calculated, na.rm = T),
Ara_mean = mean (Aragonite_Sat_W , na.rm = T),
Ara_sd = sd (Aragonite_Sat_W , na.rm = T),
n())
Seasonal_Stats<-Seasonal_Stats %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_Stats- Season stats (monthly mean)
Seasonal_Stats2<- ddply (Month_Stats, .(Zone, Sub_region, Season),
summarise,
Tem_M = mean (Tem_mean, na.rm = T),
#Tem_sd = sd (Tem_mean, na.rm = T),
Tem_se = std.error (Tem_mean, na.rm = T),
Sal_M = mean (Sal_mean, na.rm = T),
#Sal_sd = sd (Sal_mean, na.rm = T),
Sal_se = std.error (Sal_mean, na.rm = T),
TA_M = mean (TA_mean, na.rm = T),
#TA_sd = sd (TA_mean, na.rm = T),
TA_se = std.error (TA_mean, na.rm = T),
DIC_M = mean (DIC_mean, na.rm = T),
#DIC_sd = sd (DIC_mean, na.rm = T),
DIC_se = std.error (DIC_mean, na.rm = T),
#nTA_M = mean (nTA_mean, na.rm = T),
#nTA_sd = sd (nTA_mean, na.rm = T),
#nDIC_M = mean (nDIC_mean, na.rm = T),
#nDIC_sd = sd (nDIC_mean, na.rm = T),
pCO2_M = mean (pCO2_mean , na.rm = T),
#pCO2_sd = sd (pCO2_mean , na.rm = T),
pCO2_se = std.error (pCO2_mean , na.rm = T),
pH_M = mean (pH_mean, na.rm = T),
#pH_sd = sd (pH_mean, na.rm = T),
pH_se = std.error (pH_mean, na.rm = T),
Ara_M = mean (Ara_mean , na.rm = T),
#Ara_sd = sd (Ara_mean , na.rm = T),
Ara_se = std.error (Ara_mean , na.rm = T),
n())
Seasonal_Stats2<-Seasonal_Stats2 %>%
mutate_if(is.numeric, round, digits=2)
Seasonal_Stats2# write.csv(Seasonal_Stats2, "FLK_results/Table_S_Season_A.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
- Yearly stats (monthly mean)
Yearly_Stats<- ddply (Month_Stats, .(Zone, Sub_region),
summarise,
Tem_Y = mean (Tem_mean, na.rm = T),
Tem_sd = sd (Tem_mean, na.rm = T),
Sal_Y = mean (Sal_mean, na.rm = T),
Sal_sd = sd (Sal_mean, na.rm = T),
TA_Y = mean (TA_mean, na.rm = T),
TA_sd = sd (TA_mean, na.rm = T),
nTA_Y = mean (nTA_mean, na.rm = T),
nTA_sd = sd (nTA_mean, na.rm = T),
nDIC_Y = mean (nDIC_mean, na.rm = T),
nDIC_sd = sd (nDIC_mean, na.rm = T),
DIC_Y = mean (DIC_mean, na.rm = T),
DIC_sd = sd (DIC_mean, na.rm = T),
pCO2_Y = mean (pCO2_mean , na.rm = T),
pCO2_sd = sd (pCO2_mean , na.rm = T),
pH_Y = mean (pH_mean, na.rm = T),
pH_sd = sd (pH_mean, na.rm = T),
Ara_Y = mean (Ara_mean , na.rm = T),
Ara_sd = sd (Ara_mean , na.rm = T),
n())
Yearly_Stats<-Yearly_Stats %>%
mutate_if(is.numeric, round, digits=2)
Yearly_Stats# write.csv(Yearly_Stats, "FLK_results/Table_1_Year_A.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
5.3 Delta months values
# 1. Get the oceanic mean values per month-year
WS.reef<-join(WS.reef, mean_Open_MY,
type = "left", by=c("MY", "Month"))
# 2. and subtract them from the reef data
# Delta TA
WS.reef$dTA_MY<-WS.reef$TA_umol_kg-WS.reef$OTA_my
# Delta DIC
WS.reef$dDIC_MY<-WS.reef$DIC_umol_kg-WS.reef$ODIC_my
# Delta nTA
WS.reef$dnTA_MY<-WS.reef$n35TA-WS.reef$OnTA_my
# Delta nDIC
WS.reef$dnDIC_MY<-WS.reef$n35DIC-WS.reef$OnDIC_my
# Delta pCO2
WS.reef$dpCO2_MY<-WS.reef$pCO2_uatm-WS.reef$OpCO2_O
# Delta pH
WS.reef$dpH_MY<-WS.reef$pH_calculated-WS.reef$OpH_O
# Delta Omega
WS.reef$dOme_MY<-WS.reef$Aragonite_Sat_W-WS.reef$OAra_O
# head(WS.reef)
#summary(WS.reef$Season, digits=2)- Delta month stats
dMonth_Stats<- ddply (WS.reef, .(Zone, Sub_region, Month2, Season, Region),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
#dTA_se = std.error (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dDIC_se = std.error (dDIC_MY, na.rm = T),
dnDIC_mean = mean (dnDIC_MY, na.rm = T),
dnDIC_sd = sd (dnDIC_MY, na.rm = T),
dpCO2_mean = mean (dpCO2_MY , na.rm = T),
dpCO2_sd = sd (dpCO2_MY , na.rm = T),
#dpCO2_se = std.error (dpCO2_MY, na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = sd (dpH_MY, na.rm = T),
#dPH_se = std.error (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = sd (dOme_MY , na.rm = T)#,
#dAra_se = std.error (dOme_MY, na.rm = T),
)
dMonth_Stats<-dMonth_Stats %>%
mutate_if(is.numeric, round, digits=3)
dMonth_Stats#write.csv(dMonth_Stats, "FLK_results/Table_S9_Month_StatsDeltas.csv", row.names = F)
Spring.data<-WS.reef[WS.reef$Season=="Spring", ]
Summer.data<-WS.reef[WS.reef$Season=="Summer", ]
Fall.data<-WS.reef[WS.reef$Season=="Fall", ]
Winter.data<-WS.reef[WS.reef$Season=="Winter", ]Seasonal range
Seasonal_range_d<-ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
dTA_max = max (dTA_mean, na.rm = T),
dTA_min = min (dTA_mean, na.rm = T),
dTA_diff = (max (dTA_mean, na.rm = T) - min (dTA_mean, na.rm = T)),
dDIC_max = max (dDIC_mean, na.rm = T),
dDIC_min = min (dDIC_mean, na.rm = T),
dDIC_diff = (max (dDIC_mean, na.rm = T) - min (dDIC_mean, na.rm = T)),
dpCO2_max = max (dpCO2_mean, na.rm = T),
dpCO2_min = min (dpCO2_mean, na.rm = T),
dpCO2_dif = (max (dpCO2_mean, na.rm = T) - min (dpCO2_mean, na.rm = T)),
dpH_max = max (dpH_mean, na.rm = T),
dpH_min = min (dpH_mean, na.rm = T),
dpH_diff = (max (dpH_mean, na.rm = T) - min (dpH_mean, na.rm = T)),
dAra_max = max (dAra_mean, na.rm = T),
dAra_min = min (dAra_mean, na.rm = T),
dAra_diff = (max (dAra_mean, na.rm = T) - min (dAra_mean, na.rm = T)),
n = n())
Seasonal_range_d<-Seasonal_range_d %>%
mutate_if(is.numeric, round, digits=3)
Seasonal_range_d#write.csv(Seasonal_range_d, "FLK_results/Table_S8_Month_mean_range_d.csv", row.names = F)- Delta month plots
Delta_plot<- ggplot(WS.reef) +
MyTheme +
facet_grid(Sub_region~Zone)+
scale_shape_manual(values=c(24, 25, 23, 22, 21))+
guides(fill=guide_legend(override.aes=list(shape=21)))+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank())
DeltaYear_plot<- ggplot(dMonth_Stats) +
MyTheme +
facet_grid(Sub_region~Region)+
Season_fill+
Season_colour+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 10,
vjust = 0.5))
Delta_plot_month<- ggplot(WS.reef) +
MyTheme +
geom_abline(slope = 0, intercept = 0, color="grey")+
facet_grid(~Zone)+
guides(fill=guide_legend(override.aes=list(shape=21)))+
scale_x_continuous(limits = c(1,12),
expand = c(0.05, 0.05),
breaks = seq(1, 12, 1),
name=expression(paste("Month of the year")))+
theme(axis.title.x = element_blank(),
legend.position = "bottom")
DeltaYear_plot2<- ggplot(dMonth_Stats) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))
DeltaYear_plot3<- ggplot(dMonth_Stats) +
MyTheme +
Zone_shapes3+
facet_grid(~Region)+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))
Frequency_plot<- ggplot(data = WS.reef) +
geom_vline(aes(xintercept = 0), linetype = "dashed")+
facet_grid(Zone~Sub_region, scales = "free_y")+ MyThemedTA
Figure Supp 4: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 4A
DTA_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
breaks = seq(-800, 800, 100),
name=expression(paste(Delta,
"TA (", mu, "mol ", ~kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dTA_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dTA_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dTA_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#DTA_exp
TA_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "TA (", mu, "mol ", ~kg^-1, ")")))+
stat_summary(aes (x=Season, y=dTA_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dTA_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#TA_delta_Y2
dTA_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-210,70),
expand = c(0.03, 0.03),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Zone, y=dTA_mean,
fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5,
position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dTA_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))+
facet_grid(~Region)
#dTA_Y3
Figure 4A: Seasonal delta Total Alkalinity (dTA) variation by region and shelf position. Mean values and 95 CI
dDIC
Figure Supp 5: Total Alkalinity (TA) variation by the region and shelf position by (A) month of the year and (B) season.
Figure 4B
dDIC_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
breaks = seq(-800, 800, 100),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dDIC_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dDIC_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dDIC_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#dDIC_exp
DIC_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
stat_summary(aes (x=Season, y=dDIC_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dDIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#DIC_delta_Y2
dDIC_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-185,130),
expand = c(0.01, 0.01),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Zone, y=dDIC_mean,
fill=Sub_region, shape = Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5,
position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dDIC_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))
#dDIC_Y3
Figure 4B: Seasonal delta Dissolved Inorganic Carbon (dDIC) variation by region and shelf position.
dDIC density
dDIC_freq_All<- Frequency_plot+
geom_density(aes (dDIC_MY), fill = "lightgray", alpha=0.5) +
scale_x_continuous(breaks = seq(-300, 300, 100),
expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
#dDIC_freq_All
dDIC_freq_pre<- Frequency_plot+
geom_density(aes (dDIC_MY, fill = Precipitation),
alpha = 0.4) +
scale_x_continuous(breaks = seq(-300, 300, 100),
expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
#dDIC_freq_pre
# dDIC_freq_sea<- Frequency_plot+
# geom_density(aes (dDIC_MY, fill = Season), alpha = 0.4) +
# scale_x_continuous(breaks = seq(-300, 300, 100),
# expression(paste(Delta, "DIC (", mu, "mol ", kg^-1, ")")))
# dDIC_freq_seadpCO2
pdCO2_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1700,2900),
expand = c(0.05, 0.05),
#breaks = seq(1800, 2800, 100),
name=expression(paste(Delta ~ pCO[2] , " (", mu, "atm)")))+
geom_smooth(aes (x=as.numeric(Month2),
y=dpCO2_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dpCO2_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dpCO2_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))
#pdCO2_exp
pCO2_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.1, 0.1),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta ~ pCO[2] , " (", mu, "atm)")))+
stat_summary(aes (x=Season, y=dpCO2_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dpCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))
#pCO2_delta_Y2
dPCO2_Y3<- DeltaYear_plot2+
scale_y_continuous(#limits = c(2150,2550),
expand = c(0.01, 0.01),
#breaks = seq(0, 2500, 50),
name=expression(paste("")))+
stat_summary(aes (x=Sub_region, y=dpCO2_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "point",
size=1.5, shape=21)+
stat_summary(aes (x=Sub_region, y=dpCO2_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2)
#dPCO2_Y3
dpH
Figure 4C
dOmega
Figure 4D
DOmega_exp<- Delta_plot_month+
scale_y_continuous(#limits = c(1,5.5),
name=(expression (paste(Delta ,
~Omega[Ar]))),
breaks = seq(-3, 3, 0.5),
expand = c(0.05, 0.05)
) +
geom_smooth(aes (x=as.numeric(Month2),
y=dOme_MY, colour=Sub_region),
span=0.8, se=F)+
stat_summary(aes (x=as.numeric(Month2),
y=dOme_MY, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.2))+
stat_summary(aes (x=as.numeric(Month2),
y=dOme_MY, fill=Sub_region),
shape=21, size=1,
fun.data = "mean_cl_boot", geom = "point",
position=position_dodge(width=0.2))+
facet_grid(Zone~.)
#DOmega_exp
om_delta_Y2<- DeltaYear_plot2+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.05, 0.05),
#breaks = seq(-800, 800, 50),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
stat_summary(aes (x=Season, y=dAra_mean, fill=Sub_region),
fun.data = "mean_cl_boot", geom = "bar",
position=position_dodge(width=0.8))+
stat_summary(aes (x=Season, y=dAra_mean, colour=Sub_region),
fun.data = "mean_cl_boot", geom = "errorbar",
width = 0.2,
position=position_dodge(width=0.8))+
theme(panel.spacing = unit(0.8, "lines"))
#om_delta_Y2
dOm_Y3<- DeltaYear_plot3+
scale_y_continuous(#limits = c(-0.9, 1),
expand = c(0.02, 0.02),
breaks = seq(-5, 5, 0.5),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
stat_summary(aes (x=Zone, y=dAra_mean,
fill=Sub_region, shape=Zone),
fun.data = "mean_cl_boot", geom = "point",
size=1.5, position=position_dodge(width=0.5))+
stat_summary(aes (x=Zone, y=dAra_mean, colour=Sub_region),
fun.data = "mean_cl_boot",
geom = "errorbar", width = 0.2,
position=position_dodge(width=0.5))
#dOm_Y3
- Figure 4: Seasonal Δ and yearly sums
Seasonality_deltas<-ggarrange(
TA_delta_Y2 +theme(legend.position = "none",
axis.text.x=element_blank()),
DIC_delta_Y2+theme(legend.position = "none",
strip.text.x = element_blank(),
axis.text.x=element_blank()),
pH_delta_Y2+theme(legend.position = "none",
strip.text.x = element_blank(),
axis.text.x=element_blank()),
om_delta_Y2+ theme(panel.spacing = unit(0.8, "lines"),
strip.text.x = element_blank()),
#TA_DIC_sum,
nrow = 4,
#labels = c("A", "B", " C"),
heights =c(1, 0.9, 0.9, 1.2))
#widths = c(1, 1, 0.9))
Seasonality_deltas
Seasonality_deltas3<-ggarrange(
dTA_my3,
dDIC_my3,
dpH_my3,
dOm_my3,
nrow = 4,
#labels = c("A", "B", " C"),
heights =c(1, 0.9, 0.9, 1))
Seasonality_deltas3
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_season.svg", plot=Seasonality_deltas, dpi = 300, width=6, height=6)
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_ICRS.svg", plot=Seasonality_deltas, dpi = 300, width=8, height=6)
#ggsave(file="FLK_results/Figures/Fig_4/Fig_4_delta_Summary.svg", plot=Seasonality_deltas3, dpi = 300, width=8, height=8)Figure 4: Seasonal inshore-ocean gradients in the Florida Keys. (A) Seasonal ΔTA. (B) Seasonal ΔDIC. (C) Seasonal ΩAr. The bars represent the mean Δ values per season, region, and shelf position (± 95 CI).
5.4 Delta season values
- Delta season stats (all data)
dSeason_Stats<- ddply (WS.reef, .(Zone, Sub_region, Season),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dnDIC_mean = mean (dnDIC_MY, na.rm = T),
#dnDIC_sd = sd (dnDIC_MY, na.rm = T),
#dpCO2_mean = mean (dpCO2_MY , na.rm = T),
#dpCO2_sd = sd (dpCO2_MY , na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = mean (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = mean (dOme_MY , na.rm = T),
n())
dSeason_Stats<-dSeason_Stats %>%
mutate_if(is.numeric, round, digits=3)
dSeason_Stats- Delta season stats (monthly mean)
dSeasonal_Stats2<- ddply (dMonth_Stats,
.(Zone, Season, Sub_region),
summarise,
dTA_M = mean (dTA_mean, na.rm = T),
dTA_sd = sd (dTA_mean, na.rm = T),
dTA_se = std.error (dTA_mean, na.rm = T),
dDIC_M = mean (dDIC_mean, na.rm = T),
dDIC_sd = sd (dDIC_mean, na.rm = T),
dDIC_se = std.error (dDIC_mean, na.rm = T),
#dnTA_M = mean (dnTA_mean, na.rm = T),
#dnTA_sd = sd (dnTA_mean, na.rm = T),
#dnDIC_M = mean (dnDIC_mean, na.rm = T),
#dnDIC_sd = sd (dnDIC_mean, na.rm = T),
dpCO2_M = mean (dpCO2_mean , na.rm = T),
dpCO2_sd = sd (dpCO2_mean , na.rm = T),
dpCO2_se = std.error (dpCO2_mean , na.rm = T),
dpH_M = mean (dpH_mean, na.rm = T),
dpH_sd = sd (dpH_mean, na.rm = T),
dpH_se = std.error (dpH_mean, na.rm = T),
dAra_M = mean (dAra_mean , na.rm = T),
dAra_sd = sd (dAra_mean , na.rm = T),
dAra_se = std.error (dAra_mean , na.rm = T),
n())
dSeasonal_Stats2<-dSeasonal_Stats2 %>%
mutate_if(is.numeric, round, digits=3)
dSeasonal_Stats2#write.csv(dSeasonal_Stats2, "FLK_results/Table_2_SeasonDelta_B.csv", row.names = F)Note: SD is NA when calculating it based on monthly meand (n=3 per season)
- Delta yearly stats (monthly mean)
dYearly_Stats<- ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
dTA_M = mean (dTA_mean, na.rm = T),
dTA_sd = sd (dTA_mean, na.rm = T),
#dTA_se = std.error (dTA_mean, na.rm = T),
dDIC_M = mean (dDIC_mean, na.rm = T),
dDIC_sd = sd (dDIC_mean, na.rm = T),
#dDIC_se = std.error (dDIC_mean, na.rm = T),
dnTA_M = mean (dnTA_mean, na.rm = T),
dnTA_sd = sd (dnTA_mean, na.rm = T),
dnDIC_M = mean (dnDIC_mean, na.rm = T),
dnDIC_sd = sd (dnDIC_mean, na.rm = T),
dpCO2_M = mean (dpCO2_mean , na.rm = T),
dpCO2_sd = sd (dpCO2_mean , na.rm = T),
#dpCO2_se = std.error (dpCO2_mean , na.rm = T),
dpH_M = mean (dpH_mean, na.rm = T),
dpH_sd = sd (dpH_mean, na.rm = T),
#dpH_se = std.error (dpH_mean, na.rm = T),
dAra_M = mean (dAra_mean , na.rm = T),
dAra_sd = sd (dAra_mean , na.rm = T),
#dAra_se = std.error (dAra_mean , na.rm = T),
n())
dYearly_Stats<-dYearly_Stats %>%
mutate_if(is.numeric, round, digits=3)
dYearly_Stats#write.csv(dYearly_Stats, "FLK_results/Table_1b_delta_year.csv", row.names = F)- Yearly summ (monthly mean)
dYear_Add<- ddply (dMonth_Stats, .(Zone, Sub_region),
summarise,
su_dTA_Y = sum (dTA_mean, na.rm = T),
#m_TA_Y = mean (dTA_mean, na.rm = T),
#sd_TA_Y = sd (dTA_mean, na.rm = T),
su_dDIC_Y = sum (dDIC_mean, na.rm = T),
#m_dDIC_Y = mean (dDIC_mean, na.rm = T),
#sd_dDIC_Y = sd (dDIC_mean, na.rm = T),
su_dpCO2_Y = sum (dpCO2_mean, na.rm = T),
#m_pCO2_Y = mean (dpCO2_mean, na.rm = T),
#sd_pCO2_Y = sd (dpCO2_mean, na.rm = T),
su_dpH_Y = sum (dpH_mean, na.rm = T),
#m_pH_Y = mean (dpH_mean, na.rm = T),
#sd_pH_Y = sd (dpH_mean, na.rm = T),
su_dAra_Y = sum (dAra_mean, na.rm = T),
#m_Ara_Y = mean (dAra_mean, na.rm = T),
#sd_Ara_Y = sd (dAra_mean, na.rm = T),
n())
dYear_Add<-dYear_Add %>%
mutate_if(is.numeric, round, digits=2)
dYear_Add#write.csv(dYear_Add, "FLK_results/Table_1_year_byMM.csv", row.names = F)- Yearly summ (indiviual years)
dYear_Add2<- ddply (WS.reef, .(Zone, Sub_region, MY, Region, Year),
summarise,
dTA_mean = mean (dTA_MY, na.rm = T),
dTA_sd = sd (dTA_MY, na.rm = T),
#dTA_se = std.error (dTA_MY, na.rm = T),
dnTA_mean = mean (dnTA_MY, na.rm = T),
dnTA_sd = sd (dnTA_MY, na.rm = T),
dDIC_mean = mean (dDIC_MY, na.rm = T),
dDIC_sd = mean (dDIC_MY, na.rm = T),
#dDIC_se = std.error (dDIC_MY, na.rm = T),
dnDIC_mean = mean (dnDIC_MY, na.rm = T),
dnDIC_sd = sd (dnDIC_MY, na.rm = T),
dpCO2_mean = mean (dpCO2_MY , na.rm = T),
dpCO2_sd = sd (dpCO2_MY , na.rm = T),
#dpCO2_se = std.error (dpCO2_MY, na.rm = T),
dpH_mean = mean (dpH_MY, na.rm = T),
dpH_sd = mean (dpH_MY, na.rm = T),
#dPH_se = std.error (dpH_MY, na.rm = T),
dAra_mean = mean (dOme_MY , na.rm = T),
dAra_sd = mean (dOme_MY , na.rm = T),
#dAra_se = std.error (dOme_MY, na.rm = T),
n())
dYear_Add2<-dYear_Add2 %>%
mutate_if(is.numeric, round, digits=3)
dYear_Add2dYear_Add2<-dYear_Add2[dYear_Add2$Year>2014, ]
dYear_Add2<-dYear_Add2[dYear_Add2$Year<2022, ]
dYear_Add3<- ddply (dYear_Add2, .(Zone, Sub_region, Year),
summarise,
su_dTA_Y = sum (dTA_mean, na.rm = T),
#m_TA_Y = mean (dTA_mean, na.rm = T),
#sd_TA_Y = sd (dTA_mean, na.rm = T),
su_dDIC_Y = sum (dDIC_mean, na.rm = T),
#m_dDIC_Y = mean (dDIC_mean, na.rm = T),
#sd_dDIC_Y = sd (dDIC_mean, na.rm = T),
su_dpCO2_Y = sum (dpCO2_mean, na.rm = T),
#m_pCO2_Y = mean (dpCO2_mean, na.rm = T),
#sd_pCO2_Y = sd (dpCO2_mean, na.rm = T),
su_dpH_Y = sum (dpH_mean, na.rm = T),
#m_pH_Y = mean (dpH_mean, na.rm = T),
#sd_pH_Y = sd (dpH_mean, na.rm = T),
su_dAra_Y = sum (dAra_mean, na.rm = T),
#m_Ara_Y = mean (dAra_mean, na.rm = T),
#sd_Ara_Y = sd (dAra_mean, na.rm = T),
n())
dYear_Add3<-dYear_Add3 %>%
mutate_if(is.numeric, round, digits=2)
dYear_Add35.5 Seasonal models
season.data<-WS.reef[WS.reef$Extreme=="Normal", ]
season.data<-droplevels(season.data)
season.data$Zone<-factor(season.data$Zone, levels = c("Offshore", "Mid channel", "Inshore"))
#summary(season.data)
inshore.season.data<-season.data[season.data$Zone=="Inshore", ]
midchanel.season.data<-season.data[season.data$Zone=="Mid channel", ]
ofshore.season.data<-season.data[season.data$Zone=="Offshore", ]LMER - TA month
hist(FLK.data_filtered$TA_umol_kg)
shapiro.test(FLK.data_filtered$TA_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: FLK.data_filtered$TA_umol_kg
## W = 0.81653, p-value < 2.2e-16#
# dTA_season<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
TA_month_0<-lmer(TA_umol_kg~ Zone * Sub_region * Month +
(1|SiteID), data=FLK.data_filtered)
#anova(TA_month_0)
TA_month<-lmer(TA_umol_kg~ Zone + Sub_region + Month +
Zone:Sub_region + Zone:Month + Sub_region:Month +
(1|SiteID), data=season.data)
#summary(dTA_season)
av.TA_month<-(anova(TA_month))
av.TA_month ranova(TA_month) #step(TA_month)
plot(TA_month)
par(mfrow=c(1,2))
qqnorm(resid(TA_month))
acf(resid(TA_month), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(TA_month, scale = TRUE)
#plot_summs(TA_month)
TA_mo.emmc<-emmeans(TA_month, ~Zone*Month, by="Sub_region")
TA_mon.emmc<-multcomp::cld(TA_mo.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
#TA_mon.emmc<-TA_mon.emmc[order(TA_mon.emmc$Zone, TA_mon.emmc$Month), ]
TA_mon.emmcLMER - TA season
hist(season.data$TA_umol_kg)
shapiro.test(season.data$TA_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$TA_umol_kg
## W = 0.84701, p-value < 2.2e-16#
# dTA_season<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
TA_season_0<-lmer(TA_umol_kg~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
anova(TA_season_0) TA_season<-lmer(TA_umol_kg~ Zone + Sub_region + Season +
Zone:Sub_region +
Zone:Season +
Sub_region:Season +
(1|SiteID), data=season.data)
anova(TA_season_0, TA_season) #summary(av.TA_season)
av.TA_season<-(anova(TA_season))
av.TA_season ranova(TA_season) #step(TA_season)
plot(TA_season)
par(mfrow=c(1,2))
qqnorm(resid(TA_season))
acf(resid(TA_season), main="acf(resid(TA Season))")
par(mfrow=c(1,1))
#summ(av.TA_season, scale = TRUE)
#plot_summs(TA_season)
TA_se.emmc<-emmeans(TA_season, ~Zone*Season, by="Sub_region")
TA_seas.emmc<-multcomp::cld(TA_se.emmc)
TA_seas.emmc TA_se.emmc2<-emmeans(TA_season, ~Sub_region*Season, by="Zone")
TA_seas.emmc2<-multcomp::cld(TA_se.emmc2)
TA_seas.emmc2LMER - all dTA - MODEL 13
hist(season.data$dTA_MY)
shapiro.test(season.data$dTA_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dTA_MY
## W = 0.82647, p-value < 2.2e-16#
# dTA_season<-lmer(dTA_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dTA_season<-lmer(dTA_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dTA_season)
av.dTA_season<-(anova(dTA_season))
av.dTA_season ranova(dTA_season) #step(dTA_season)
plot(dTA_season)
par(mfrow=c(1,2))
qqnorm(resid(dTA_season))
acf(resid(dTA_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dTA_season, scale = TRUE)
#plot_summs(dTA_season) dTA_ses.emmc<-emmeans(dTA_season, ~Season*Sub_region, by="Zone")
dTA_ses.emmc<-multcomp::cld(dTA_ses.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dTA_ses.emmc<-dTA_ses.emmc[order(dTA_ses.emmc$Zone,
dTA_ses.emmc$Season,
dTA_ses.emmc$Sub_region), ]
dTA_ses.emmc dTA_ses.emmc[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal dTA") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 1.05 | 11.74 | 223.40 | -22.10 | 24.19 | 1 |
| Winter | UK | 3.89 | 10.07 | 257.16 | -15.94 | 23.72 | 1 |
| Winter | MK | 4.60 | 9.68 | 230.62 | -14.47 | 23.66 | 1 |
| Winter | LK | 1.94 | 8.31 | 220.73 | -14.43 | 18.32 | 1 |
| Spring | BB | -3.90 | 11.17 | 183.03 | -25.93 | 18.13 | 1 |
| Spring | UK | -1.98 | 9.51 | 214.78 | -20.72 | 16.76 | 1 |
| Spring | MK | -7.46 | 8.76 | 162.74 | -24.76 | 9.84 | 1 |
| Spring | LK | -11.50 | 7.54 | 157.14 | -26.39 | 3.39 | 1 |
| Summer | BB | -5.51 | 12.08 | 246.64 | -29.30 | 18.28 | 1 |
| Summer | UK | -4.77 | 10.28 | 277.89 | -25.00 | 15.47 | 1 |
| Summer | MK | -24.72 | 9.86 | 246.64 | -44.14 | -5.29 | 1 |
| Summer | LK | -10.43 | 8.54 | 246.64 | -27.26 | 6.39 | 1 |
| Fall | BB | -0.83 | 10.90 | 170.54 | -22.35 | 20.68 | 1 |
| Fall | UK | -0.58 | 9.50 | 216.63 | -19.31 | 18.15 | 1 |
| Fall | MK | -1.32 | 8.76 | 162.74 | -18.62 | 15.98 | 1 |
| Fall | LK | -3.35 | 7.62 | 164.25 | -18.40 | 11.69 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 8.02 | 12.08 | 246.64 | -15.77 | 31.81 | 34 |
| Winter | UK | 10.66 | 12.45 | 271.51 | -13.86 | 35.17 | 34 |
| Winter | MK | 25.24 | 9.68 | 230.62 | 6.17 | 44.31 | 4 |
| Winter | LK | 13.55 | 8.90 | 171.14 | -4.01 | 31.11 | 4 |
| Spring | BB | -8.62 | 12.09 | 243.16 | -32.43 | 15.19 | 234 |
| Spring | UK | -3.03 | 11.74 | 223.36 | -26.17 | 20.12 | 234 |
| Spring | MK | -37.30 | 8.64 | 154.39 | -54.36 | -20.24 | 123 |
| Spring | LK | -49.11 | 8.40 | 139.56 | -65.72 | -32.50 | 12 |
| Summer | BB | -13.78 | 12.08 | 246.64 | -37.57 | 10.01 | 234 |
| Summer | UK | -5.58 | 12.87 | 297.18 | -30.91 | 19.74 | 234 |
| Summer | MK | -73.30 | 9.86 | 246.64 | -92.72 | -53.87 | 1 |
| Summer | LK | -76.55 | 9.86 | 246.64 | -95.97 | -57.12 | 1 |
| Fall | BB | 4.06 | 11.16 | 186.76 | -17.94 | 26.07 | 34 |
| Fall | UK | 0.63 | 11.74 | 223.36 | -22.52 | 23.77 | 234 |
| Fall | MK | 7.17 | 8.89 | 172.13 | -10.39 | 24.72 | 4 |
| Fall | LK | -11.66 | 8.52 | 146.93 | -28.49 | 5.17 | 234 |
| Inshore | |||||||
| Winter | BB | 40.60 | 11.74 | 223.41 | 17.45 | 63.74 | 67 |
| Winter | UK | 48.74 | 10.51 | 298.97 | 28.07 | 69.41 | 67 |
| Winter | MK | 57.00 | 10.27 | 281.16 | 36.77 | 77.22 | 7 |
| Winter | LK | 7.36 | 9.19 | 189.81 | -10.77 | 25.48 | 56 |
| Spring | BB | -62.57 | 11.17 | 183.07 | -84.61 | -40.54 | 34 |
| Spring | UK | -57.07 | 9.86 | 246.64 | -76.50 | -37.64 | 34 |
| Spring | MK | -80.92 | 8.40 | 139.55 | -97.53 | -64.31 | 3 |
| Spring | LK | -124.49 | 8.29 | 132.95 | -140.89 | -108.09 | 2 |
| Summer | BB | -46.25 | 12.08 | 246.64 | -70.04 | -22.45 | 34 |
| Summer | UK | -35.23 | 9.86 | 246.64 | -54.66 | -15.81 | 45 |
| Summer | MK | -148.77 | 10.06 | 262.68 | -168.58 | -128.95 | 12 |
| Summer | LK | -183.91 | 10.27 | 281.21 | -204.14 | -163.69 | 1 |
| Fall | BB | 26.25 | 10.65 | 158.44 | 5.20 | 47.29 | 67 |
| Fall | UK | 44.42 | 9.86 | 246.64 | 24.99 | 63.85 | 67 |
| Fall | MK | 16.80 | 8.90 | 171.08 | -0.77 | 34.36 | 67 |
| Fall | LK | -63.22 | 8.52 | 146.93 | -80.05 | -46.39 | 34 |
dTA_year.emmc<-emmeans(dTA_season, ~Sub_region*Zone)
dTA_year.emmc<-multcomp::cld(dTA_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dTA_year.emmc<-dTA_year.emmc[order(dTA_year.emmc$Zone,
dTA_year.emmc$Sub_region), ]
#dTA_year.emmc
dTA_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year dTA") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | -2.30 | 6.34 | 21.07 | -15.49 | 10.89 | 34 |
| UK | -0.86 | 5.39 | 24.81 | -11.97 | 10.25 | 4 |
| MK | -7.23 | 5.13 | 20.50 | -17.92 | 3.47 | 34 |
| LK | -5.84 | 4.44 | 20.23 | -15.09 | 3.41 | 4 |
| Mid-Channel | ||||||
| BB | -2.58 | 6.51 | 23.52 | -16.03 | 10.88 | 34 |
| UK | 0.67 | 6.67 | 25.91 | -13.05 | 14.39 | 4 |
| MK | -19.55 | 5.14 | 20.51 | -30.24 | -8.85 | 234 |
| LK | -30.94 | 4.98 | 18.14 | -41.40 | -20.49 | 23 |
| Inshore | ||||||
| BB | -10.49 | 6.32 | 20.76 | -23.64 | 2.65 | 234 |
| UK | 0.21 | 5.47 | 26.46 | -11.03 | 11.46 | 4 |
| MK | -38.97 | 5.21 | 21.68 | -49.78 | -28.17 | 2 |
| LK | -91.07 | 5.05 | 19.19 | -101.64 | -80.49 | 1 |
Figure 4A: Model
dTA_ses_2.emmc<-as.data.frame(dTA_ses.emmc)
dTA_ye_3.emmc<-as.data.frame(dTA_year.emmc)
dTA_ye_3.emmc$Season<-"Mean"
dTA_all_4<-rbind(dTA_ses_2.emmc, dTA_ye_3.emmc)
dTA_all_4$Zone<-factor(dTA_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearTA<- ggplot(dTA_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "TA (", mu, "mol ", ~kg^-1, ")")))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearTA
LMER - dTA Inshore
# Inshore dTA
hist(inshore.season.data$dTA_MY)
shapiro.test(inshore.season.data$dTA_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dTA_MY
## W = 0.9743, p-value = 1.971e-06 dTA_season_in<-lmer(dTA_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dTA_season_in)
av.dTA_season_in<-anova(dTA_season_in)
av.dTA_season_in ranova(dTA_season_in) #step(dTA_season_in)
plot(dTA_season_in)
par(mfrow=c(1,2))
qqnorm(resid(dTA_season_in))
acf(resid(dTA_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dTA_season_in, scale = TRUE)
#plot_summs(dTA_season_in)
dTA_ses_in.emmc<-emmeans(dTA_season_in, ~ Sub_region*Season)
dTA_ses_in.emmc<-multcomp::cld(dTA_ses_in.emmc)
#dTA_ses_in.emmc<-as.data.frame(dTA_ses_in.emmc[complete.cases(dTA_ses_in.emmc),])
dTA_ses_in.emmc<-dTA_ses_in.emmc[order(dTA_ses_in.emmc$Season,
dTA_ses_in.emmc$Sub_region), ]
dTA_ses_in.emmcLMER - DIC season
hist(season.data$DIC_umol_kg)
shapiro.test(season.data$DIC_umol_kg) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$DIC_umol_kg
## W = 0.91454, p-value < 2.2e-16 DIC_season_0<-lmer(DIC_umol_kg~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(DIC_season_0)
av.DIC_season<-(anova(DIC_season_0))
av.DIC_season ranova(DIC_season_0) #step(DIC_season_0)
plot(DIC_season_0)
par(mfrow=c(1,2))
qqnorm(resid(DIC_season_0))
acf(resid(DIC_season_0), main="acf(resid(DIC Season))")
par(mfrow=c(1,1))
#summ(av.TA_season, scale = TRUE)
#plot_summs(av.DIC_season)
DIC_se.emmc<-emmeans(DIC_season_0, ~Zone*Season, by="Sub_region")
DIC_seas.emmc<-multcomp::cld(DIC_se.emmc)
DIC_seas.emmc DIC_se.emmc2<-emmeans(DIC_season_0, ~Sub_region*Season, by="Zone")
DIC_seas.emmc2<-multcomp::cld(TA_se.emmc2)
DIC_seas.emmc2LMER - all dDIC - MODEL 14
# Inshore Omega
hist(season.data$dDIC_MY)
shapiro.test(season.data$dDIC_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dDIC_MY
## W = 0.87909, p-value < 2.2e-16 # dDIC_season<-lmer(dDIC_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dDIC_season<-lmer(dDIC_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dDIC_season)
av.dDIC_season<-anova(dDIC_season)
av.dDIC_season ranova(dDIC_season) step(dDIC_season)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 50 -6071.5 12243
## (1 | SiteID) 0 49 -6074.0 12246 5.0185 1 0.02508 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Zone:Sub_region:Season 0 102157 5675.4 18 1113.1 2.4361 0.000722
##
## Zone:Sub_region:Season ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Zone * Sub_region * Season + (1 | SiteID) plot(dDIC_season)
par(mfrow=c(1,2))
qqnorm(resid(dDIC_season))
acf(resid(dDIC_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_season, scale = TRUE)
#plot_summs(dDIC_season)
dDIC_ses.emmc<-emmeans(dDIC_season, ~Season*Sub_region, by="Zone")
dDIC_ses.emmc<-multcomp::cld(dDIC_ses.emmc)
#dDIC_ses.emmc<-as.data.frame(dDIC_ses.emmc[complete.cases(dDIC_ses.emmc),])
dDIC_ses.emmc<-dDIC_ses.emmc[order(dDIC_ses.emmc$Zone,
dDIC_ses.emmc$Season,
dDIC_ses.emmc$Sub_region), ]
#dDIC_ses.emmc
dDIC_ses.emmc[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal dDIC") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | -1.15 | 12.79 | 151.53 | -26.42 | 24.11 | 1 |
| Winter | UK | -0.09 | 10.92 | 175.49 | -21.64 | 21.47 | 1 |
| Winter | MK | 7.93 | 10.53 | 156.21 | -12.87 | 28.73 | 1 |
| Winter | LK | 7.32 | 8.84 | 138.04 | -10.16 | 24.79 | 1 |
| Spring | BB | -0.53 | 12.23 | 126.75 | -24.73 | 23.67 | 1 |
| Spring | UK | -1.51 | 10.36 | 146.50 | -21.99 | 18.97 | 1 |
| Spring | MK | -13.60 | 9.63 | 112.46 | -32.69 | 5.48 | 1 |
| Spring | LK | -6.09 | 8.29 | 109.38 | -22.52 | 10.35 | 1 |
| Summer | BB | 5.88 | 13.12 | 166.46 | -20.02 | 31.78 | 1 |
| Summer | UK | -2.98 | 11.12 | 188.57 | -24.92 | 18.96 | 1 |
| Summer | MK | -20.44 | 10.71 | 166.46 | -41.58 | 0.71 | 1 |
| Summer | LK | 0.35 | 9.67 | 193.08 | -18.73 | 19.43 | 1 |
| Fall | BB | 3.55 | 11.96 | 117.87 | -20.14 | 27.24 | 1 |
| Fall | UK | 2.73 | 10.36 | 147.09 | -17.73 | 23.20 | 1 |
| Fall | MK | 0.39 | 9.63 | 112.46 | -18.70 | 19.47 | 1 |
| Fall | LK | 1.17 | 8.37 | 113.66 | -15.41 | 17.76 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 10.65 | 13.12 | 166.46 | -15.25 | 36.55 | 45 |
| Winter | UK | 1.74 | 13.48 | 183.28 | -24.87 | 28.34 | 345 |
| Winter | MK | 24.46 | 10.53 | 156.21 | 3.66 | 45.26 | 45 |
| Winter | LK | 31.79 | 9.77 | 118.05 | 12.45 | 51.13 | 5 |
| Spring | BB | -21.90 | 13.13 | 165.36 | -47.82 | 4.02 | 12345 |
| Spring | UK | -29.06 | 12.79 | 151.50 | -54.33 | -3.79 | 1234 |
| Spring | MK | -65.71 | 9.51 | 107.15 | -84.57 | -46.86 | 1 |
| Spring | LK | -53.48 | 9.28 | 97.74 | -71.90 | -35.06 | 123 |
| Summer | BB | 4.03 | 13.12 | 166.46 | -21.87 | 29.92 | 45 |
| Summer | UK | -3.00 | 13.90 | 201.96 | -30.41 | 24.41 | 2345 |
| Summer | MK | -59.92 | 10.71 | 166.46 | -81.07 | -38.78 | 12 |
| Summer | LK | -57.79 | 11.12 | 189.74 | -79.72 | -35.86 | 123 |
| Fall | BB | 22.16 | 12.21 | 127.83 | -2.00 | 46.33 | 45 |
| Fall | UK | 15.78 | 12.79 | 151.50 | -9.49 | 41.04 | 45 |
| Fall | MK | 22.59 | 9.76 | 118.32 | 3.26 | 41.92 | 45 |
| Fall | LK | 5.03 | 9.39 | 102.32 | -13.60 | 23.66 | 45 |
| Inshore | |||||||
| Winter | BB | 46.86 | 12.79 | 151.53 | 21.60 | 72.13 | 345 |
| Winter | UK | 43.10 | 11.35 | 202.57 | 20.72 | 65.47 | 34 |
| Winter | MK | 55.50 | 11.12 | 189.71 | 33.57 | 77.43 | 45 |
| Winter | LK | 27.84 | 10.05 | 130.68 | 7.96 | 47.73 | 34 |
| Spring | BB | -90.97 | 12.23 | 126.78 | -115.16 | -66.77 | 1 |
| Spring | UK | -125.12 | 10.71 | 166.46 | -146.26 | -103.98 | 1 |
| Spring | MK | -130.41 | 9.28 | 97.73 | -148.83 | -111.98 | 1 |
| Spring | LK | -131.64 | 9.18 | 93.55 | -149.86 | -113.42 | 1 |
| Summer | BB | -10.42 | 13.12 | 166.46 | -36.31 | 15.48 | 23 |
| Summer | UK | -25.40 | 10.71 | 166.46 | -46.55 | -4.26 | 2 |
| Summer | MK | -122.39 | 10.91 | 177.35 | -143.91 | -100.86 | 1 |
| Summer | LK | -112.23 | 11.58 | 218.92 | -135.06 | -89.40 | 1 |
| Fall | BB | 73.29 | 11.72 | 109.74 | 50.06 | 96.52 | 45 |
| Fall | UK | 96.30 | 10.71 | 166.46 | 75.16 | 117.44 | 5 |
| Fall | MK | 50.25 | 9.77 | 118.03 | 30.92 | 69.59 | 45 |
| Fall | LK | -7.47 | 9.39 | 102.32 | -26.10 | 11.16 | 23 |
dDIC_year.emmc<-emmeans(dDIC_season, ~Sub_region*Zone)
dDIC_year.emmc<-multcomp::cld(dDIC_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dDIC_year.emmc<-dDIC_year.emmc[order(dDIC_year.emmc$Zone,
dDIC_year.emmc$Sub_region), ]
#dDIC_year.emmc
dDIC_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year dDIC") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | 1.94 | 7.69 | 20.95 | -14.06 | 17.93 | 3 |
| UK | -0.46 | 6.47 | 23.60 | -13.82 | 12.90 | 3 |
| MK | -6.43 | 6.24 | 20.49 | -19.42 | 6.56 | 23 |
| LK | 0.69 | 5.42 | 20.64 | -10.59 | 11.97 | 3 |
| Mid-Channel | ||||||
| BB | 3.73 | 7.84 | 22.67 | -12.50 | 19.96 | 3 |
| UK | -3.64 | 7.98 | 24.37 | -20.10 | 12.83 | 23 |
| MK | -19.65 | 6.24 | 20.50 | -32.64 | -6.65 | 23 |
| LK | -18.61 | 6.15 | 19.32 | -31.47 | -5.76 | 23 |
| Inshore | ||||||
| BB | 4.69 | 7.67 | 20.71 | -11.27 | 20.65 | 3 |
| UK | -2.78 | 6.54 | 24.73 | -16.26 | 10.70 | 3 |
| MK | -36.76 | 6.30 | 21.34 | -49.85 | -23.67 | 12 |
| LK | -55.87 | 6.22 | 20.24 | -68.84 | -42.91 | 1 |
Figure 4B: Model
dDIC_ses_2.emmc<-as.data.frame(dDIC_ses.emmc)
dDIC_ye_3.emmc<-as.data.frame(dDIC_year.emmc)
dDIC_ye_3.emmc$Season<-"Mean"
dDIC_all_4<-rbind(dDIC_ses_2.emmc, dDIC_ye_3.emmc)
dDIC_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearDIC<- ggplot(dDIC_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
#breaks = seq(-800, 800, 50),
name=expression(paste(Delta, "DIC (", mu, "mol ", ~kg^-1, ")")))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearDIC
LMER - dDIC Inshore
# Inshore dDIC
hist(inshore.season.data$dDIC_MY)
shapiro.test(inshore.season.data$dDIC_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dDIC_MY
## W = 0.99486, p-value = 0.2191 dDIC_season_in<-lmer(dDIC_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dDIC_season_in)
av.dDIC_season_in<-anova(dDIC_season_in)
av.dDIC_season_in ranova(dDIC_season_in) #step(dDIC_season_in)
plot(dDIC_season_in)
par(mfrow=c(1,1))
qqnorm(resid(dDIC_season_in))
acf(resid(dDIC_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_season_in, scale = TRUE)
#plot_summs(dDIC_season_in)
dDIC_ses_in.emmc<-emmeans(dDIC_season_in, ~ Sub_region*Season)
dDIC_ses_in.emmc<-multcomp::cld(dDIC_ses_in.emmc)
#dDIC_ses_in.emmc<-as.data.frame(dDIC_ses_in.emmc[complete.cases(dDIC_ses_in.emmc),])
dDIC_ses_in.emmc<-dDIC_ses_in.emmc[order(dDIC_ses_in.emmc$Season,
dDIC_ses_in.emmc$Sub_region), ]
dDIC_ses_in.emmcLMER - all dpCO2
# Inshore Omega
hist(season.data$dpCO2_MY)
shapiro.test(season.data$dpCO2_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dpCO2_MY
## W = 0.91489, p-value < 2.2e-16 # dpCO2_season<-lmer(dpCO2_MY~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dpCO2_season<-lmer(dpCO2_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dpCO2_season)
av.dPCO2_season<-anova(dpCO2_season)
av.dPCO2_season ranova(dpCO2_season) step(dpCO2_season)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 50 -6083.0 12266
## (1 | SiteID) 0 49 -6086.3 12271 6.5586 1 0.01044 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value
## Zone:Sub_region:Season 1 64511 3584 18 1109.73 1.4821
## Zone:Sub_region 2 15206 2534 6 21.57 1.0401
## Zone:Season 0 610146 101691 6 1129.41 41.7412
## Sub_region:Season 0 69763 7751 9 1128.46 3.1818
## Pr(>F)
## Zone:Sub_region:Season 0.0877942 .
## Zone:Sub_region 0.4272177
## Zone:Season < 2.2e-16 ***
## Sub_region:Season 0.0008284 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dpCO2_MY ~ Zone + Sub_region + Season + (1 | SiteID) + Zone:Season + Sub_region:Season plot(dpCO2_season)
par(mfrow=c(1,2))
qqnorm(resid(dpCO2_season))
acf(resid(dpCO2_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dpCO2_season)
dpCO2_ses.emmc<-emmeans(dpCO2_season, ~Zone*Sub_region*Season)
dpCO2_ses.emmc<-multcomp::cld(dpCO2_ses.emmc)
#dpCO2_ses.emmc<-as.data.frame(dpCO2_ses.emmc[complete.cases(dpCO2_ses.emmc),])
dpCO2_ses.emmc<-dpCO2_ses.emmc[order(dpCO2_ses.emmc$Zone, dpCO2_ses.emmc$Season), ]
dpCO2_ses.emmc #summ(dpCO2_season, scale = TRUE)LMER - dpCO2 Inshore
# Inshore dDIC
hist(inshore.season.data$dpCO2_MY)
shapiro.test(inshore.season.data$dpCO2_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$dpCO2_MY
## W = 0.98334, p-value = 0.0001773 dpCO2_season_in<-lmer(dpCO2_MY~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(dpCO2_season_in)
av.dPCO2_season_in<-anova(dpCO2_season_in)
av.dPCO2_season_in ranova(dpCO2_season_in) #step(dpCO2_season_in)
plot(dpCO2_season_in)
par(mfrow=c(1,2))
qqnorm(resid(dpCO2_season_in))
acf(resid(dpCO2_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dpCO2_season_in, scale = TRUE)
#plot_summs(dpCO2_season_in)
dpCO2_ses_in.emmc<-emmeans(dpCO2_season_in, ~ Sub_region*Season)
dpCO2_ses_in.emmc<-multcomp::cld(dpCO2_ses_in.emmc)
#dpCO2_ses_in.emmc<-as.data.frame(dpCO2_ses_in.emmc[complete.cases(dpCO2_ses_in.emmc),])
dpCO2_ses_in.emmc<-dpCO2_ses_in.emmc[order(dpCO2_ses_in.emmc$Season,
dpCO2_ses_in.emmc$Sub_region), ]
dpCO2_ses_in.emmcLMER - all Omega
# Inshore Omega
hist(season.data$Aragonite_Sat_W)
shapiro.test(season.data$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$Aragonite_Sat_W
## W = 0.97667, p-value = 6.107e-13 # Omega_season<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
Omega_season<-lmer(Aragonite_Sat_W~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(Omega_season)
av.Ome_season<-anova(Omega_season)
av.Ome_season ranova(Omega_season) #step(Omega_season)
plot(Omega_season)
par(mfrow=c(1,2))
qqnorm(resid(Omega_season))
acf(resid(Omega_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(Omega_season)
# Ara_ses.emmc<-emmeans(Omega_season, ~Zone*Sub_region*Season)
# Ara_ses.emmc<-multcomp::cld(Ara_ses.emmc)
# Ara_ses.emmc<-as.data.frame(Ara_ses.emmc[complete.cases(Ara_ses.emmc),])
# Ara_ses.emmc<-Ara_ses.emmc[order(Ara_ses.emmc$Zone, Ara_ses.emmc$Season), ]
# Ara_ses.emmc
Ara_ses.emmc<-emmeans(Omega_season, ~Zone*Season, by="Sub_region")
Ara_ses.emmc<-multcomp::cld(Ara_ses.emmc)
Ara_ses.emmc Ara_ses.emmc2<-emmeans(Omega_season, ~Sub_region*Season, by="Zone")
Ara_ses.emmc2<-multcomp::cld(Ara_ses.emmc2)
Ara_ses.emmc2LMER - all dOmega - MODEL 16
# Inshore Omega
hist(season.data$dOme_MY)
shapiro.test(season.data$dOme_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dOme_MY
## W = 0.94836, p-value < 2.2e-16 # Omega_season<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dOmega_season<-lmer(dOme_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dOmega_season)
av.dOme_season<-anova(dOmega_season)
av.dOme_season ranova(dOmega_season) #step(dOmega_season)
plot(dOmega_season)
par(mfrow=c(1,2))
qqnorm(resid(dOmega_season))
acf(resid(dOmega_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dOmega_season)
dAra_ses.emmc<-emmeans(dOmega_season, ~Zone*Season, by="Sub_region")
dAra_ses.emmc<-multcomp::cld(dAra_ses.emmc)
dAra_ses.emmcdAra_ses.emmc1<-emmeans(dOmega_season, ~Sub_region*Season, by="Zone")
dAra_ses.emmc1<-multcomp::cld(dAra_ses.emmc1)
dAra_ses.emmc1dAra_ses.emmc2<-emmeans(dOmega_season, ~Season*Sub_region, by="Zone")
dAra_ses.emmc2<-multcomp::cld(dAra_ses.emmc2)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dAra_ses.emmc2<-dAra_ses.emmc2[order(dAra_ses.emmc2$Zone,
dAra_ses.emmc2$Season,
dAra_ses.emmc2$Sub_region), ]
dAra_ses.emmc2[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal d Omega") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 0.02 | 0.07 | 188.90 | -0.12 | 0.16 | 1 |
| Winter | UK | 0.04 | 0.06 | 218.36 | -0.08 | 0.16 | 1 |
| Winter | MK | -0.04 | 0.06 | 194.94 | -0.16 | 0.07 | 1 |
| Winter | LK | -0.07 | 0.05 | 187.22 | -0.17 | 0.03 | 1 |
| Spring | BB | -0.04 | 0.07 | 156.03 | -0.17 | 0.10 | 1 |
| Spring | UK | -0.01 | 0.06 | 182.03 | -0.12 | 0.11 | 1 |
| Spring | MK | 0.06 | 0.05 | 138.40 | -0.05 | 0.17 | 1 |
| Spring | LK | -0.06 | 0.05 | 134.05 | -0.16 | 0.03 | 1 |
| Summer | BB | -0.13 | 0.07 | 208.26 | -0.27 | 0.02 | 1 |
| Summer | UK | -0.03 | 0.06 | 235.49 | -0.15 | 0.10 | 1 |
| Summer | MK | -0.07 | 0.06 | 208.26 | -0.19 | 0.05 | 1 |
| Summer | LK | -0.15 | 0.05 | 241.97 | -0.26 | -0.04 | 1 |
| Fall | BB | -0.05 | 0.07 | 145.13 | -0.18 | 0.08 | 1 |
| Fall | UK | -0.04 | 0.06 | 183.21 | -0.15 | 0.08 | 1 |
| Fall | MK | -0.02 | 0.05 | 138.40 | -0.13 | 0.09 | 1 |
| Fall | LK | -0.06 | 0.05 | 139.79 | -0.15 | 0.04 | 1 |
| Mid-Channel | |||||||
| Winter | BB | -0.04 | 0.07 | 208.26 | -0.19 | 0.10 | 12345 |
| Winter | UK | 0.08 | 0.08 | 229.47 | -0.07 | 0.23 | 2345 |
| Winter | MK | 0.00 | 0.06 | 194.94 | -0.12 | 0.11 | 234 |
| Winter | LK | -0.19 | 0.05 | 145.50 | -0.30 | -0.08 | 12 |
| Spring | BB | 0.13 | 0.07 | 206.04 | -0.01 | 0.28 | 345 |
| Spring | UK | 0.27 | 0.07 | 188.86 | 0.13 | 0.41 | 45 |
| Spring | MK | 0.27 | 0.05 | 131.50 | 0.17 | 0.38 | 5 |
| Spring | LK | 0.02 | 0.05 | 119.26 | -0.09 | 0.12 | 2345 |
| Summer | BB | -0.21 | 0.07 | 208.26 | -0.36 | -0.07 | 12 |
| Summer | UK | -0.03 | 0.08 | 252.17 | -0.18 | 0.13 | 12345 |
| Summer | MK | -0.20 | 0.06 | 208.26 | -0.32 | -0.08 | 123 |
| Summer | LK | -0.32 | 0.06 | 237.70 | -0.44 | -0.19 | 1 |
| Fall | BB | -0.20 | 0.07 | 158.32 | -0.34 | -0.07 | 12 |
| Fall | UK | -0.17 | 0.07 | 188.86 | -0.31 | -0.02 | 123 |
| Fall | MK | -0.17 | 0.05 | 146.09 | -0.28 | -0.06 | 123 |
| Fall | LK | -0.19 | 0.05 | 125.28 | -0.29 | -0.09 | 12 |
| Inshore | |||||||
| Winter | BB | -0.06 | 0.07 | 188.91 | -0.20 | 0.08 | 567 |
| Winter | UK | 0.05 | 0.06 | 253.35 | -0.08 | 0.17 | 67 |
| Winter | MK | 0.03 | 0.06 | 237.66 | -0.10 | 0.15 | 67 |
| Winter | LK | -0.24 | 0.06 | 161.40 | -0.35 | -0.13 | 3456 |
| Spring | BB | 0.24 | 0.07 | 156.06 | 0.11 | 0.38 | 78 |
| Spring | UK | 0.65 | 0.06 | 208.26 | 0.53 | 0.77 | 9 |
| Spring | MK | 0.44 | 0.05 | 119.25 | 0.34 | 0.55 | 89 |
| Spring | LK | -0.01 | 0.05 | 113.80 | -0.11 | 0.09 | 67 |
| Summer | BB | -0.41 | 0.07 | 208.26 | -0.55 | -0.26 | 234 |
| Summer | UK | -0.12 | 0.06 | 208.26 | -0.24 | 0.00 | 456 |
| Summer | MK | -0.41 | 0.06 | 221.97 | -0.53 | -0.29 | 234 |
| Summer | LK | -0.80 | 0.07 | 274.41 | -0.93 | -0.67 | 1 |
| Fall | BB | -0.48 | 0.07 | 134.85 | -0.61 | -0.35 | 123 |
| Fall | UK | -0.52 | 0.06 | 208.26 | -0.64 | -0.40 | 123 |
| Fall | MK | -0.35 | 0.05 | 145.46 | -0.46 | -0.24 | 345 |
| Fall | LK | -0.65 | 0.05 | 125.28 | -0.75 | -0.54 | 12 |
dOm_year.emmc<-emmeans(dOmega_season, ~Sub_region*Zone)
dOm_year.emmc<-multcomp::cld(dOm_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dOm_year.emmc<-dOm_year.emmc[order(dOm_year.emmc$Zone,
dOm_year.emmc$Sub_region), ]
#dDIC_year.emmc
dOm_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year d Omega") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | -0.05 | 0.04 | 20.88 | -0.13 | 0.04 | 234 |
| UK | -0.01 | 0.03 | 24.10 | -0.08 | 0.06 | 234 |
| MK | -0.02 | 0.03 | 20.35 | -0.09 | 0.05 | 234 |
| LK | -0.08 | 0.03 | 20.92 | -0.14 | -0.02 | 234 |
| Mid-Channel | ||||||
| BB | -0.08 | 0.04 | 22.98 | -0.17 | 0.01 | 234 |
| UK | 0.04 | 0.04 | 25.04 | -0.05 | 0.13 | 4 |
| MK | -0.02 | 0.03 | 20.37 | -0.09 | 0.04 | 234 |
| LK | -0.17 | 0.03 | 18.97 | -0.24 | -0.10 | 2 |
| Inshore | ||||||
| BB | -0.18 | 0.04 | 20.60 | -0.26 | -0.09 | 23 |
| UK | 0.01 | 0.03 | 25.50 | -0.06 | 0.09 | 34 |
| MK | -0.07 | 0.03 | 21.38 | -0.14 | 0.00 | 234 |
| LK | -0.42 | 0.03 | 20.05 | -0.49 | -0.36 | 1 |
Figure 4D: Model
dAra_ses_2.emmc<-as.data.frame(dAra_ses.emmc2)
dAra_ye_3.emmc<-as.data.frame(dOm_year.emmc)
dAra_ye_3.emmc$Season<-"Mean"
dAra_all_4<-rbind(dAra_ses_2.emmc, dAra_ye_3.emmc)
dAra_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearAra<- ggplot(dAra_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.03, 0.03),
breaks = seq(-6, 6, 0.3),
name=(expression (paste(Delta ,
~Omega[Ar]))))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearAra
LMER - Omega Inshore
# Inshore dOmega
hist(inshore.season.data$Aragonite_Sat_W)
shapiro.test(inshore.season.data$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$Aragonite_Sat_W
## W = 0.9897, p-value = 0.006908 Omega_season_in<-lmer(Aragonite_Sat_W~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(Omega_season_in)
av.Ome_season_in<-anova(Omega_season_in)
av.Ome_season_in ranova(Omega_season_in) #step(Omega_season_in)
plot(Omega_season_in)
par(mfrow=c(1,2))
qqnorm(resid(Omega_season_in))
acf(resid(Omega_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(dDIC_season_in, scale = TRUE)
#plot_summs(Omega_season_in)
omega_ses_in.emmc<-emmeans(Omega_season_in, ~ Sub_region*Season)
omega_ses_in.emmc<-multcomp::cld(omega_ses_in.emmc)
#omega_ses_in.emmc<-as.data.frame(omega_ses_in.emmc[complete.cases(omega_ses_in.emmc),])
omega_ses_in.emmc<-omega_ses_in.emmc[order(omega_ses_in.emmc$Season,
omega_ses_in.emmc$Sub_region), ]
omega_ses_in.emmcLMER - all pH
# Inshore Omega
hist(season.data$pH_calculated)
shapiro.test(season.data$pH_calculated) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$pH_calculated
## W = 0.97823, p-value = 2.099e-12 # pH_season<-lmer(pH_calculated~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
pH_season<-lmer(pH_calculated~ Zone * Sub_region * Season *
(1|SiteID), data=season.data)
#summary(pH_season)
av.pH_season<-anova(pH_season)
av.pH_season ranova(pH_season) #step(pH_season)
plot(pH_season)
par(mfrow=c(1,2))
qqnorm(resid(pH_season))
acf(resid(pH_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(pH_season, scale = TRUE)
#plot_summs(pH_season)
pH_ses.emmc<-emmeans(pH_season, ~Zone*Sub_region*Season)
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmc pH_ses.emmc<-emmeans(pH_season, ~Zone, by = "Sub_region")
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
#pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmc pH_ses.emmc<-emmeans(pH_season, ~Zone)
pH_ses.emmc<-multcomp::cld(pH_ses.emmc)
pH_ses.emmc<-as.data.frame(pH_ses.emmc[complete.cases(pH_ses.emmc),])
#pH_ses.emmc<-pH_ses.emmc[order(pH_ses.emmc$Zone, pH_ses.emmc$Season), ]
pH_ses.emmcLMER - all dpH - MODEL 15
# Inshore Omega
hist(season.data$dpH_MY)
shapiro.test(season.data$dpH_MY) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: season.data$dpH_MY
## W = 0.93766, p-value < 2.2e-16 # dpH_season<-lmer(Aragonite_Sat_W~ Zone + Sub_region + Season +
# Zone:Sub_region + Zone:Season + Sub_region:Season+
# (1|SiteID), data=season.data)
dpH_season<-lmer(dpH_MY~ Zone * Sub_region * Season +
(1|SiteID), data=season.data)
#summary(dpH_season)
av.dpH_season<-anova(dpH_season)
av.dpH_season ranova(dpH_season) #step(dpH_season)
plot(dpH_season)
par(mfrow=c(1,2))
qqnorm(resid(dpH_season))
acf(resid(dpH_season), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#plot_summs(dpH_season)
dpH_ses.emmc<-emmeans(dpH_season, ~Zone*Season, by="Sub_region")
dpH_ses.emmc<-multcomp::cld(dpH_ses.emmc)
dpH_ses.emmcdpH_ses.emmc<-emmeans(dpH_season, ~Sub_region)
dpH_ses.emmc<-multcomp::cld(dpH_ses.emmc)
dpH_ses.emmcdpH_ses.emmc2<-emmeans(dpH_season, ~Season*Sub_region, by="Zone")
dpH_ses.emmc2<-multcomp::cld(dpH_ses.emmc2)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dpH_ses.emmc2<-dpH_ses.emmc2[order(dpH_ses.emmc2$Zone,
dpH_ses.emmc2$Season,
dpH_ses.emmc2$Sub_region), ]
#dpH_ses.emmc2
dpH_ses.emmc2[,-c(3)] %>% kable("html", digits=2, row.names = F,
caption = "Seasonal d pH") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 16) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 17, 32) %>%
pack_rows(., "Inshore", 33, 48)| Season | Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|---|
| Offshore | |||||||
| Winter | BB | 0.01 | 0.01 | 145.67 | -0.01 | 0.03 | 1 |
| Winter | UK | 0.01 | 0.01 | 168.69 | -0.01 | 0.03 | 1 |
| Winter | MK | 0.00 | 0.01 | 150.14 | -0.01 | 0.02 | 1 |
| Winter | LK | 0.00 | 0.01 | 144.97 | -0.02 | 0.01 | 1 |
| Spring | BB | 0.00 | 0.01 | 122.12 | -0.02 | 0.02 | 1 |
| Spring | UK | 0.00 | 0.01 | 140.91 | -0.02 | 0.02 | 1 |
| Spring | MK | 0.01 | 0.01 | 108.39 | -0.01 | 0.03 | 1 |
| Spring | LK | -0.01 | 0.01 | 105.48 | -0.02 | 0.01 | 1 |
| Summer | BB | -0.02 | 0.01 | 159.91 | -0.04 | 0.00 | 1 |
| Summer | UK | -0.01 | 0.01 | 181.14 | -0.03 | 0.01 | 1 |
| Summer | MK | -0.01 | 0.01 | 159.91 | -0.03 | 0.01 | 1 |
| Summer | LK | -0.02 | 0.01 | 185.37 | -0.04 | 0.00 | 1 |
| Fall | BB | 0.00 | 0.01 | 113.58 | -0.02 | 0.02 | 1 |
| Fall | UK | 0.00 | 0.01 | 141.43 | -0.02 | 0.02 | 1 |
| Fall | MK | 0.00 | 0.01 | 108.39 | -0.01 | 0.02 | 1 |
| Fall | LK | 0.00 | 0.01 | 109.55 | -0.02 | 0.01 | 1 |
| Mid-Channel | |||||||
| Winter | BB | 0.01 | 0.01 | 159.91 | -0.01 | 0.03 | 123456 |
| Winter | UK | 0.03 | 0.01 | 176.00 | 0.00 | 0.05 | 3456 |
| Winter | MK | 0.01 | 0.01 | 150.14 | 0.00 | 0.03 | 3456 |
| Winter | LK | -0.01 | 0.01 | 113.74 | -0.03 | 0.00 | 12345 |
| Spring | BB | 0.02 | 0.01 | 158.94 | 0.00 | 0.05 | 456 |
| Spring | UK | 0.03 | 0.01 | 145.64 | 0.01 | 0.06 | 56 |
| Spring | MK | 0.04 | 0.01 | 103.33 | 0.02 | 0.05 | 6 |
| Spring | LK | 0.01 | 0.01 | 94.36 | -0.01 | 0.02 | 23456 |
| Summer | BB | -0.03 | 0.01 | 159.91 | -0.05 | -0.01 | 123 |
| Summer | UK | -0.01 | 0.01 | 193.98 | -0.04 | 0.01 | 12345 |
| Summer | MK | -0.03 | 0.01 | 159.91 | -0.05 | -0.01 | 12 |
| Summer | LK | -0.04 | 0.01 | 182.17 | -0.06 | -0.02 | 1 |
| Fall | BB | -0.02 | 0.01 | 123.05 | -0.04 | 0.00 | 12345 |
| Fall | UK | -0.01 | 0.01 | 145.64 | -0.04 | 0.01 | 12345 |
| Fall | MK | -0.01 | 0.01 | 113.97 | -0.03 | 0.01 | 12345 |
| Fall | LK | -0.02 | 0.01 | 98.72 | -0.03 | 0.00 | 1234 |
| Inshore | |||||||
| Winter | BB | 0.01 | 0.01 | 145.67 | -0.01 | 0.04 | 456 |
| Winter | UK | 0.03 | 0.01 | 194.52 | 0.02 | 0.05 | 567 |
| Winter | MK | 0.02 | 0.01 | 182.15 | 0.00 | 0.04 | 56 |
| Winter | LK | -0.01 | 0.01 | 125.83 | -0.03 | 0.01 | 345 |
| Spring | BB | 0.05 | 0.01 | 122.14 | 0.03 | 0.07 | 678 |
| Spring | UK | 0.10 | 0.01 | 159.91 | 0.08 | 0.11 | 8 |
| Spring | MK | 0.07 | 0.01 | 94.35 | 0.05 | 0.08 | 78 |
| Spring | LK | 0.02 | 0.01 | 90.36 | 0.00 | 0.03 | 56 |
| Summer | BB | -0.06 | 0.01 | 159.91 | -0.08 | -0.04 | 12 |
| Summer | UK | -0.03 | 0.01 | 159.91 | -0.05 | -0.01 | 234 |
| Summer | MK | -0.05 | 0.01 | 170.33 | -0.07 | -0.03 | 23 |
| Summer | LK | -0.10 | 0.01 | 210.11 | -0.12 | -0.08 | 1 |
| Fall | BB | -0.05 | 0.01 | 105.80 | -0.07 | -0.03 | 23 |
| Fall | UK | -0.06 | 0.01 | 159.91 | -0.08 | -0.05 | 12 |
| Fall | MK | -0.03 | 0.01 | 113.72 | -0.05 | -0.01 | 234 |
| Fall | LK | -0.06 | 0.01 | 98.72 | -0.07 | -0.04 | 2 |
dpH_year.emmc<-emmeans(dpH_season, ~Sub_region*Zone)
dpH_year.emmc<-multcomp::cld(dpH_year.emmc)
#dTA_ses.emmc<-as.data.frame(dTA_ses.emmc[complete.cases(dTA_ses.emmc),])
dpH_year.emmc<-dpH_year.emmc[order(dpH_year.emmc$Zone,
dpH_year.emmc$Sub_region), ]
#dDIC_year.emmc
dpH_year.emmc[,-c(2)] %>% kable("html", digits=2, row.names = F,
caption = "Year d pH") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Offshore", 1, 4) %>% # groups rows with zone label
pack_rows(., "Mid-Channel", 5, 8) %>%
pack_rows(., "Inshore", 9, 12)| Sub_region | emmean | SE | df | lower.CL | upper.CL | .group |
|---|---|---|---|---|---|---|
| Offshore | ||||||
| BB | 0.00 | 0.01 | 20.91 | -0.02 | 0.01 | 2 |
| UK | 0.00 | 0.01 | 23.46 | -0.01 | 0.01 | 2 |
| MK | 0.00 | 0.01 | 20.45 | -0.01 | 0.01 | 2 |
| LK | -0.01 | 0.00 | 20.94 | -0.02 | 0.00 | 2 |
| Mid-Channel | ||||||
| BB | 0.00 | 0.01 | 22.56 | -0.02 | 0.01 | 2 |
| UK | 0.01 | 0.01 | 24.20 | -0.01 | 0.02 | 2 |
| MK | 0.00 | 0.01 | 20.46 | -0.01 | 0.01 | 2 |
| LK | -0.02 | 0.01 | 19.33 | -0.03 | -0.01 | 12 |
| Inshore | ||||||
| BB | -0.01 | 0.01 | 20.68 | -0.03 | 0.00 | 12 |
| UK | 0.01 | 0.01 | 24.55 | 0.00 | 0.02 | 2 |
| MK | 0.00 | 0.01 | 21.28 | -0.01 | 0.01 | 2 |
| LK | -0.04 | 0.01 | 20.22 | -0.05 | -0.03 | 1 |
Figure 4C: Model
dpH_ses_2.emmc<-as.data.frame(dpH_ses.emmc2)
dpH_ye_3.emmc<-as.data.frame(dpH_year.emmc)
dpH_ye_3.emmc$Season<-"Mean"
dpH_all_4<-rbind(dpH_ses_2.emmc, dpH_ye_3.emmc)
dpH_all_4$Zone<-factor(dDIC_all_4$Zone,
levels=c("Inshore" , "Mid channel", "Offshore"))
DeltaYearpH<- ggplot(dpH_all_4, aes(x=Season, y=emmean, fill=Sub_region)) +
MyTheme +
#facet_grid(Zone~., scales = "free_y")+
facet_grid(~Zone, scales="free" )+
geom_hline(yintercept = 0, color="gray")+
theme(axis.title.x = element_blank(),
legend.position = "bottom",
axis.text.x = element_text(angle = 0))+
scale_y_continuous(#limits = c(-350,340),
expand = c(0.01, 0.01),
breaks = seq(-6, 6, 0.05),
name=(expression (paste(Delta ,
~pH[]))))+
geom_bar(position = "dodge", color="black",
stat = "summary",
fun = "mean")+
geom_errorbar(aes(ymin=emmean-SE, ymax=emmean+SE), width=0.2,
position=position_dodge(.9))
DeltaYearpH
LMER - pH Inshore
# Inshore pH
hist(inshore.season.data$pH_calculated)
shapiro.test(inshore.season.data$pH_calculated) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: inshore.season.data$pH_calculated
## W = 0.99421, p-value = 0.1375 pH_season_in<-lmer(pH_calculated~ Sub_region * Season +
(1|SiteID), data=inshore.season.data)
#summary(pH_season_in)
av.pH_season_in<-anova(pH_season_in)
av.pH_season_in ranova(pH_season_in) #step(pH_season_in)
plot(pH_season_in)
par(mfrow=c(1,2))
qqnorm(resid(pH_season_in))
acf(resid(pH_season_in), main="acf(resid(dTA Season))")
par(mfrow=c(1,1))
#summ(pH_season_in, scale = TRUE)
#plot_summs(pH_season_in)
pH_ses_in.emmc<-emmeans(pH_season_in, ~ Sub_region*Season)
pH_ses_in.emmc<-multcomp::cld(pH_ses_in.emmc)
#pH_ses_in.emmc<-as.data.frame(pH_ses_in.emmc[complete.cases(pH_ses_in.emmc),])
pH_ses_in.emmc<-pH_ses_in.emmc[order(pH_ses_in.emmc$Season,
pH_ses_in.emmc$Sub_region), ]
pH_ses_in.emmcSummary all data
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.dTA_season <- data.frame(av.dTA_season)
av.dDIC_season <- data.frame(av.dDIC_season)
av.dPCO2_season <- data.frame(av.dPCO2_season)
av.dOme_season <- data.frame(av.dOme_season)
av.dpH_season <- data.frame(av.dpH_season)
# Putting all into one dataframe/table
anova_results_all <- data.frame(cbind
(c("Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season",
"Zone", "Sub_region", "Season", "Zone:Sub_region", "Zone:Season",
"Sub_region:Season", "Zone:Sub_region:Season"),
rbind(av.dTA_season, av.dDIC_season, av.dPCO2_season,
av.dOme_season, av.dpH_season )))
colnames(anova_results_all) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_results_all) <- NULL
# create HTML table using kableExtra
anova_results_all %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "d TA", 1, 7) %>% # groups rows with label
pack_rows(., "d DIC", 8, 14) %>% # groups rows with label
pack_rows(., "d pCO2", 15, 21) %>% # groups rows with label
pack_rows(., "d Omega", 22, 28) %>% # groups rows with label
pack_rows(., "d pH", 29, 35) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| d TA | ||||||
| Zone | 147511.18 | 73755.59 | 2 | 24.33 | 33.83 | 0.00 |
| Sub_region | 255038.82 | 85012.94 | 3 | 24.08 | 39.00 | 0.00 |
| Season | 857202.03 | 285734.01 | 3 | 1118.93 | 131.07 | 0.00 |
| Zone:Sub_region | 194809.81 | 32468.30 | 6 | 24.08 | 14.89 | 0.00 |
| Zone:Season | 564721.55 | 94120.26 | 6 | 1118.94 | 43.17 | 0.00 |
| Sub_region:Season | 161239.24 | 17915.47 | 9 | 1118.88 | 8.22 | 0.00 |
| Zone:Sub_region:Season | 85053.62 | 4725.20 | 18 | 1118.89 | 2.17 | 0.00 |
| d DIC | ||||||
| Zone | 50385.20 | 25192.60 | 2 | 23.49 | 10.81 | 0.00 |
| Sub_region | 85646.72 | 28548.91 | 3 | 23.38 | 12.25 | 0.00 |
| Season | 1361232.88 | 453744.29 | 3 | 1113.08 | 194.76 | 0.00 |
| Zone:Sub_region | 65760.45 | 10960.07 | 6 | 23.37 | 4.70 | 0.00 |
| Zone:Season | 1013441.05 | 168906.84 | 6 | 1113.08 | 72.50 | 0.00 |
| Sub_region:Season | 174759.19 | 19417.69 | 9 | 1113.05 | 8.33 | 0.00 |
| Zone:Sub_region:Season | 102157.02 | 5675.39 | 18 | 1113.06 | 2.44 | 0.00 |
| d pCO2 | ||||||
| Zone | 16149.26 | 8074.63 | 2 | 21.97 | 3.34 | 0.05 |
| Sub_region | 62914.07 | 20971.36 | 3 | 21.87 | 8.67 | 0.00 |
| Season | 908279.53 | 302759.84 | 3 | 1109.76 | 125.20 | 0.00 |
| Zone:Sub_region | 17081.00 | 2846.83 | 6 | 21.87 | 1.18 | 0.35 |
| Zone:Season | 618840.71 | 103140.12 | 6 | 1109.76 | 42.65 | 0.00 |
| Sub_region:Season | 70185.63 | 7798.40 | 9 | 1109.73 | 3.22 | 0.00 |
| Zone:Sub_region:Season | 64511.25 | 3583.96 | 18 | 1109.73 | 1.48 | 0.09 |
| d Omega | ||||||
| Zone | 2.27 | 1.14 | 2 | 21.43 | 14.50 | 0.00 |
| Sub_region | 6.84 | 2.28 | 3 | 21.29 | 29.06 | 0.00 |
| Season | 32.27 | 10.76 | 3 | 1109.53 | 137.17 | 0.00 |
| Zone:Sub_region | 2.86 | 0.48 | 6 | 21.29 | 6.08 | 0.00 |
| Zone:Season | 20.30 | 3.38 | 6 | 1109.54 | 43.15 | 0.00 |
| Sub_region:Season | 2.60 | 0.29 | 9 | 1109.49 | 3.68 | 0.00 |
| Zone:Sub_region:Season | 1.93 | 0.11 | 18 | 1109.50 | 1.37 | 0.14 |
| d pH | ||||||
| Zone | 0.01 | 0.00 | 2 | 22.29 | 1.91 | 0.17 |
| Sub_region | 0.07 | 0.02 | 3 | 22.19 | 14.15 | 0.00 |
| Season | 0.68 | 0.23 | 3 | 1110.09 | 132.93 | 0.00 |
| Zone:Sub_region | 0.03 | 0.00 | 6 | 22.19 | 2.73 | 0.04 |
| Zone:Season | 0.40 | 0.07 | 6 | 1110.10 | 39.14 | 0.00 |
| Sub_region:Season | 0.04 | 0.00 | 9 | 1110.06 | 2.74 | 0.00 |
| Zone:Sub_region:Season | 0.04 | 0.00 | 18 | 1110.07 | 1.22 | 0.24 |
#export_summs(dTA_season, dDIC_season, dpCO2_season, scale = TRUE)
Seasonal_models<-list("d TA" = dTA_season,
"d DIC" = dDIC_season,
"d pCO2" = dpCO2_season,
"Omega" = Omega_season,
"pH" = pH_season)
modelsummary(Seasonal_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Seasonal and spatial models'#,
#coef_map=factor_order
)| d TA | d DIC | d pCO2 | Omega | pH | |
|---|---|---|---|---|---|
| (Intercept) | 1.046 | −1.154 | −7.599 | 3.699*** | 8.074*** |
| (11.744) | (12.788) | (13.287) | (0.071) | (0.012) | |
| ZoneMid channel | 6.972 | 11.804 | 1.346 | −0.061 | −0.002 |
| (16.848) | (18.319) | (19.024) | (0.101) | (0.017) | |
| ZoneInshore | 39.553* | 48.017** | 4.049 | −0.083 | 0.006 |
| (16.609) | (18.085) | (18.791) | (0.100) | (0.016) | |
| Sub_regionUK | 2.844 | 1.067 | −1.212 | −0.009 | −0.003 |
| (15.469) | (16.815) | (17.460) | (0.093) | (0.015) | |
| Sub_regionMK | 3.549 | 9.085 | 5.807 | −0.057 | −0.004 |
| (15.218) | (16.565) | (17.209) | (0.091) | (0.015) | |
| Sub_regionLK | 0.897 | 8.471 | 9.595 | −0.110 | −0.010 |
| (14.386) | (15.544) | (16.277) | (0.086) | (0.014) | |
| SeasonSpring | −4.947 | 0.623 | 9.927 | 0.056 | −0.036* |
| (15.590) | (16.118) | (16.422) | (0.089) | (0.015) | |
| SeasonSummer | −6.558 | 7.034 | 33.200+ | 0.178+ | −0.075*** |
| (16.264) | (16.814) | (17.130) | (0.095) | (0.016) | |
| SeasonFall | −1.881 | 4.705 | 12.232 | 0.059 | −0.035* |
| (15.403) | (15.923) | (16.223) | (0.090) | (0.015) | |
| ZoneMid channelSub_regionUK | −0.203 | −9.981 | −15.883 | 0.121 | 0.020 |
| (23.243) | (25.231) | (26.187) | (0.139) | (0.023) | |
| ZoneInshoreSub_regionUK | 5.298 | −4.833 | −18.158 | 0.105 | 0.020 |
| (22.080) | (23.979) | (24.890) | (0.133) | (0.022) | |
| ZoneMid channelSub_regionMK | 13.672 | 4.725 | −6.314 | 0.118 | 0.014 |
| (21.706) | (23.607) | (24.518) | (0.130) | (0.022) | |
| ZoneInshoreSub_regionMK | 12.850 | −0.447 | −11.098 | 0.157 | 0.014 |
| (21.796) | (23.696) | (24.607) | (0.131) | (0.022) | |
| ZoneMid channelSub_regionLK | 4.635 | 12.672 | 18.773 | −0.036 | −0.007 |
| (20.785) | (22.561) | (23.534) | (0.125) | (0.021) | |
| ZoneInshoreSub_regionLK | −34.138+ | −27.489 | 12.904 | −0.069 | −0.011 |
| (20.719) | (22.498) | (23.471) | (0.125) | (0.021) | |
| ZoneMid channelSeasonSpring | −11.688 | −33.176 | −31.337 | 0.215+ | 0.031 |
| (22.708) | (23.479) | (23.921) | (0.129) | (0.022) | |
| ZoneInshoreSeasonSpring | −98.226*** | −138.451*** | −67.932** | 0.398** | 0.048* |
| (22.048) | (22.795) | (23.224) | (0.125) | (0.021) | |
| ZoneMid channelSeasonSummer | −15.240 | −13.658 | 7.610 | −0.021 | −0.007 |
| (23.174) | (23.957) | (24.407) | (0.135) | (0.023) | |
| ZoneInshoreSeasonSummer | −80.288*** | −64.312** | 48.003* | −0.196 | −0.047* |
| (23.001) | (23.778) | (24.225) | (0.134) | (0.022) | |
| ZoneMid channelSeasonFall | −2.072 | 6.809 | 17.624 | −0.088 | −0.013 |
| (22.097) | (22.843) | (23.273) | (0.129) | (0.021) | |
| ZoneInshoreSeasonFall | −12.472 | 21.724 | 69.956** | −0.357** | −0.053* |
| (21.663) | (22.395) | (22.816) | (0.126) | (0.021) | |
| Sub_regionUKSeasonSpring | −0.927 | −2.048 | −1.717 | 0.044 | 0.004 |
| (20.544) | (21.247) | (21.649) | (0.118) | (0.020) | |
| Sub_regionMKSeasonSpring | −7.109 | −22.158 | −21.626 | 0.161 | 0.019 |
| (20.015) | (20.693) | (21.083) | (0.114) | (0.019) | |
| Sub_regionLKSeasonSpring | −8.497 | −14.026 | −4.731 | 0.079 | 0.003 |
| (18.952) | (19.499) | (19.965) | (0.109) | (0.018) | |
| Sub_regionUKSeasonSummer | −2.100 | −9.926 | −12.532 | 0.114 | 0.013 |
| (21.410) | (22.134) | (22.551) | (0.125) | (0.021) | |
| Sub_regionMKSeasonSummer | −22.755 | −35.401 | −25.781 | 0.119 | 0.015 |
| (21.037) | (21.748) | (22.157) | (0.122) | (0.020) | |
| Sub_regionLKSeasonSummer | −5.819 | −14.001 | −12.088 | 0.102 | 0.011 |
| (19.920) | (20.685) | (21.168) | (0.117) | (0.020) | |
| Sub_regionUKSeasonFall | −2.590 | −1.883 | −2.761 | 0.012 | 0.002 |
| (20.395) | (21.085) | (21.482) | (0.119) | (0.020) | |
| Sub_regionMKSeasonFall | −4.033 | −12.248 | −12.350 | 0.085 | 0.012 |
| (19.870) | (20.542) | (20.928) | (0.116) | (0.019) | |
| Sub_regionLKSeasonFall | −3.416 | −10.847 | −11.405 | 0.114 | 0.011 |
| (18.830) | (19.370) | (19.833) | (0.110) | (0.018) | |
| ZoneMid channelSub_regionUKSeasonSpring | 3.876 | 3.803 | 9.623 | 0.013 | −0.017 |
| (31.118) | (32.179) | (32.787) | (0.178) | (0.030) | |
| ZoneInshoreSub_regionUKSeasonSpring | −1.712 | −28.339 | −22.426 | 0.260 | 0.030 |
| (29.322) | (30.322) | (30.894) | (0.167) | (0.028) | |
| ZoneMid channelSub_regionMKSeasonSpring | −38.793 | −35.462 | 1.763 | −0.066 | −0.019 |
| (28.785) | (29.760) | (30.321) | (0.164) | (0.027) | |
| ZoneInshoreSub_regionMKSeasonSpring | −27.637 | −25.920 | 4.822 | −0.060 | −0.008 |
| (28.405) | (29.367) | (29.920) | (0.162) | (0.027) | |
| ZoneMid channelSub_regionLKSeasonSpring | −37.531 | −38.693 | −9.099 | −0.022 | −0.006 |
| (27.725) | (28.600) | (29.207) | (0.159) | (0.026) | |
| ZoneInshoreSub_regionLKSeasonSpring | −20.178 | −7.628 | 6.718 | −0.174 | −0.017 |
| (27.248) | (28.106) | (28.704) | (0.156) | (0.026) | |
| ZoneMid channelSub_regionUKSeasonSummer | 7.655 | 11.814 | 10.293 | −0.036 | −0.013 |
| (32.125) | (33.211) | (33.835) | (0.187) | (0.031) | |
| ZoneInshoreSub_regionUKSeasonSummer | 4.972 | −1.293 | −9.457 | 0.079 | −0.001 |
| (30.292) | (31.317) | (31.907) | (0.176) | (0.029) | |
| ZoneMid channelSub_regionMKSeasonSummer | −53.984+ | −42.356 | 11.077 | −0.163 | −0.025 |
| (29.885) | (30.895) | (31.476) | (0.174) | (0.029) | |
| ZoneInshoreSub_regionMKSeasonSummer | −96.163** | −85.210** | −17.181 | −0.216 | −0.012 |
| (30.016) | (31.031) | (31.615) | (0.175) | (0.029) | |
| ZoneMid channelSub_regionLKSeasonSummer | −62.480* | −68.959* | −15.707 | −0.053 | −0.005 |
| (28.859) | (30.045) | (30.675) | (0.169) | (0.028) | |
| ZoneInshoreSub_regionLKSeasonSummer | −98.607*** | −68.791* | 12.371 | −0.299+ | −0.027 |
| (28.954) | (30.172) | (30.804) | (0.170) | (0.028) | |
| ZoneMid channelSub_regionUKSeasonFall | −3.487 | 4.409 | 14.884 | −0.107 | −0.017 |
| (30.668) | (31.705) | (32.301) | (0.178) | (0.030) | |
| ZoneInshoreSub_regionUKSeasonFall | 12.622 | 28.658 | 44.740 | −0.141 | −0.029 |
| (29.029) | (30.012) | (30.577) | (0.169) | (0.028) | |
| ZoneMid channelSub_regionMKSeasonFall | −10.085 | −1.136 | 5.312 | −0.117 | −0.013 |
| (28.385) | (29.345) | (29.897) | (0.165) | (0.028) | |
| ZoneInshoreSub_regionMKSeasonFall | −21.816 | −19.427 | −16.350 | −0.045 | 0.003 |
| (28.262) | (29.218) | (29.768) | (0.164) | (0.027) | |
| ZoneMid channelSub_regionLKSeasonFall | −17.841 | −27.429 | −21.551 | 0.047 | 0.010 |
| (27.283) | (28.139) | (28.736) | (0.159) | (0.027) | |
| ZoneInshoreSub_regionLKSeasonFall | −52.807+ | −50.897+ | −24.516 | −0.087 | 0.005 |
| (27.030) | (27.877) | (28.471) | (0.157) | (0.026) | |
| SD (Intercept SiteID) | 4.397 | 7.272 | 8.276 | 0.036 | 0.005 |
| SD (Observations) | 46.691 | 48.267 | 49.175 | 0.272 | 0.045 |
| Num.Obs. | 1184 | 1179 | 1177 | 1191 | 1191 |
| R2 Marg. | 0.500 | 0.535 | 0.383 | 0.451 | 0.457 |
| R2 Cond. | 0.504 | 0.545 | 0.400 | 0.460 | 0.464 |
| AIC | 12214.1 | 12243.0 | 12266.0 | 527.1 | −3564.8 |
| BIC | 12467.9 | 12496.6 | 12519.6 | 781.2 | −3310.6 |
| ICC | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| RMSE | 45.63 | 47.08 | 47.94 | 0.27 | 0.04 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Summary all inshore
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.dTA_season_in <- data.frame(av.dTA_season_in)
av.dDIC_season_in <- data.frame(av.dDIC_season_in)
av.dPCO2_season_in <- data.frame(av.dPCO2_season_in)
av.Ome_season_in <- data.frame(av.Ome_season_in)
av.pH_season_in <- data.frame(av.pH_season_in)
# Putting all into one dataframe/table
anova_results_In <- data.frame(cbind
(c("Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season",
"Sub_region", "Season", "Sub_region:Season"),
rbind(av.dTA_season_in, av.dDIC_season_in,
av.dPCO2_season_in,
av.Ome_season_in, av.pH_season_in)))
colnames(anova_results_In) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_results_In) <- NULL
# create HTML table using kableExtra
anova_results_In %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "d TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "d DIC inshore", 4, 6) %>% # groups rows with label
pack_rows(., "d pCO2 inshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega inshore", 10, 12) %>% # groups rows with label
pack_rows(., "pH inshore", 13, 15) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| d TA inshore | ||||||
| Sub_region | 450697.69 | 150232.56 | 3 | 8.42 | 32.03 | 0.00 |
| Season | 1250626.86 | 416875.62 | 3 | 371.65 | 88.88 | 0.00 |
| Sub_region:Season | 166830.83 | 18536.76 | 9 | 371.64 | 3.95 | 0.00 |
| d DIC inshore | ||||||
| Sub_region | 121639.36 | 40546.45 | 3 | 7.81 | 7.96 | 0.01 |
| Season | 2120498.90 | 706832.97 | 3 | 369.01 | 138.78 | 0.00 |
| Sub_region:Season | 181134.87 | 20126.10 | 9 | 369.01 | 3.95 | 0.00 |
| d pCO2 inshore | ||||||
| Sub_region | 48532.82 | 16177.61 | 3 | 7.24 | 3.15 | 0.09 |
| Season | 1336460.93 | 445486.98 | 3 | 368.48 | 86.75 | 0.00 |
| Sub_region:Season | 108532.96 | 12059.22 | 9 | 368.48 | 2.35 | 0.01 |
| Omega inshore | ||||||
| Sub_region | 6.03 | 2.01 | 3 | 7.07 | 14.60 | 0.00 |
| Season | 40.79 | 13.60 | 3 | 374.36 | 98.84 | 0.00 |
| Sub_region:Season | 3.52 | 0.39 | 9 | 374.36 | 2.85 | 0.00 |
| pH inshore | ||||||
| Sub_region | 0.07 | 0.02 | 3 | 7.44 | 6.18 | 0.02 |
| Season | 1.32 | 0.44 | 3 | 374.69 | 109.72 | 0.00 |
| Sub_region:Season | 0.07 | 0.01 | 9 | 374.70 | 1.86 | 0.06 |
#export_summs(dTA_season, dDIC_season, dpCO2_season, scale = TRUE)
Seasonal_models_in<-list("d TA" = dTA_season_in,
"d DIC" = dDIC_season_in,
"d pCO2" = dpCO2_season_in,
"Omega" = Omega_season_in,
"pH" = pH_season_in)
modelsummary(Seasonal_models_in, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Seasonal and spatial models for inshore reefs'#,
#coef_map=factor_order
)| d TA | d DIC | d pCO2 | Omega | pH | |
|---|---|---|---|---|---|
| (Intercept) | 40.632* | 46.843* | −3.618 | 3.617*** | 8.080*** |
| (16.867) | (19.066) | (19.926) | (0.099) | (0.017) | |
| Sub_regionUK | 8.140 | −3.738 | −19.226 | 0.094 | 0.017 |
| (22.663) | (25.473) | (26.556) | (0.132) | (0.022) | |
| Sub_regionMK | 16.388 | 8.657 | −5.210 | 0.099 | 0.009 |
| (22.437) | (25.251) | (26.340) | (0.131) | (0.022) | |
| Sub_regionLK | −33.045 | −19.049 | 22.510 | −0.179 | −0.021 |
| (21.397) | (24.255) | (25.380) | (0.126) | (0.021) | |
| SeasonSpring | −103.117*** | −137.861*** | −58.117* | 0.455*** | 0.012 |
| (22.865) | (23.833) | (23.932) | (0.121) | (0.021) | |
| SeasonSummer | −86.879*** | −57.259* | 81.270** | −0.018 | −0.122*** |
| (23.855) | (24.860) | (24.964) | (0.129) | (0.022) | |
| SeasonFall | −14.359 | 26.433 | 82.201*** | −0.298* | −0.088*** |
| (22.344) | (23.284) | (23.380) | (0.121) | (0.021) | |
| Sub_regionUKSeasonSpring | −2.726 | −30.364 | −24.107 | 0.303+ | 0.034 |
| (30.686) | (31.985) | (32.120) | (0.162) | (0.028) | |
| Sub_regionMKSeasonSpring | −34.810 | −48.046 | −16.696 | 0.100 | 0.011 |
| (29.563) | (30.811) | (30.939) | (0.158) | (0.027) | |
| Sub_regionLKSeasonSpring | −28.905 | −21.590 | 2.108 | −0.095 | −0.013 |
| (28.713) | (29.930) | (30.056) | (0.153) | (0.026) | |
| Sub_regionUKSeasonSummer | 2.874 | −11.247 | −22.133 | 0.194 | 0.012 |
| (31.431) | (32.758) | (32.895) | (0.170) | (0.029) | |
| Sub_regionMKSeasonSummer | −118.938*** | −120.630*** | −43.065 | −0.096 | 0.004 |
| (31.403) | (32.727) | (32.863) | (0.170) | (0.029) | |
| Sub_regionLKSeasonSummer | −104.496*** | −82.765* | 0.301 | −0.197 | −0.016 |
| (30.818) | (32.477) | (32.613) | (0.169) | (0.029) | |
| Sub_regionUKSeasonFall | 10.007 | 26.763 | 41.889 | −0.128 | −0.027 |
| (30.300) | (31.579) | (31.710) | (0.164) | (0.028) | |
| Sub_regionMKSeasonFall | −25.859 | −31.677 | −28.721 | 0.040 | 0.015 |
| (29.476) | (30.722) | (30.851) | (0.160) | (0.027) | |
| Sub_regionLKSeasonFall | −56.447* | −61.698* | −35.879 | 0.027 | 0.015 |
| (28.442) | (29.644) | (29.768) | (0.154) | (0.026) | |
| SD (Intercept SiteID) | 4.142 | 11.295 | 13.772 | 0.058 | 0.010 |
| SD (Observations) | 68.485 | 71.367 | 71.662 | 0.371 | 0.063 |
| Num.Obs. | 393 | 391 | 391 | 397 | 397 |
| R2 Marg. | 0.517 | 0.562 | 0.423 | 0.503 | 0.481 |
| R2 Cond. | 0.519 | 0.573 | 0.444 | 0.515 | 0.492 |
| AIC | 4344.4 | 4356.2 | 4360.7 | 416.9 | −931.7 |
| BIC | 4415.9 | 4427.7 | 4432.2 | 488.6 | −860.0 |
| ICC | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| RMSE | 67.00 | 69.58 | 69.81 | 0.36 | 0.06 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Figure 4: Models
Figure_4<-ggarrange(DeltaYearTA+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
DeltaYearDIC+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
DeltaYearpH + theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
DeltaYearAra+ theme(legend.position = "bottom",
strip.text.x = element_blank(),
panel.spacing = unit(0.8, "lines")),
labels = c("A", "B", "C", "D"),
ncol = 1, nrow = 4, heights = c(1, 0.9, 0.9, 1.2))
Figure_4
#ggsave(file="FLK_results/Figures/Fig_4/Figure_4.svg", plot=Figure_4, dpi = 300, width=8, height=7)6 LMER long trend MODELS
The F statistic in a regression model is used to test whether there is a linear model of the data - overall , or equivalently that your model is no better than a model with just the intercept.
The t statistic for a coefficient in a linear model tests the H0 that the true coefficient is 0.
*The t-statistic is telling you about each coefficient individually. You can have a model with a low overall linear fit, but one individual coefficient may be relatively contributing a lot to explanation the variation in the DV that is explained on the assumption that there is a linear model.
Data and plots prep
Trends.data<-FLK.data_filtered[FLK.data_filtered$Year>2014, ]
#summary(Trends.data)
Reef.Trends.data<-WS.reef[WS.reef$Year>2014, ]
Reef.Trends.data<-Reef.Trends.data[Reef.Trends.data$Extreme=="Normal", ]
#summary(Reef.Trends.data$Extreme)
Trends.ocean<-Trends.data[Trends.data$Zone=="Oceanic", ]
Trends.offshore<-Reef.Trends.data[Reef.Trends.data$Zone=="Offshore", ]
Trends.midchanel<-Reef.Trends.data[Reef.Trends.data$Zone=="Mid channel", ]
Trends.inshore<-Reef.Trends.data[Reef.Trends.data$Zone=="Inshore", ]
Long_plot<- ggplot(Trends.data) +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank())+
scale_x_date(date_breaks = "2 years",
date_labels = "%Y",
expand = c(0.02, 0.02))
Long_plot2<- ggplot() +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank(),
axis.text.x = element_text(angle = 0, vjust = 0.5))+
scale_x_date(date_breaks = "2 years",
date_labels = "%Y",
expand = c(0.02, 0.02))
xs <- as.Date(as.character("2018-11-20"))
factor_order <- c('Year' = 'Year',
'(Intercept)' = 'Intercept',
'Sub_regionUK' = 'Upper Keys',
'Sub_regionMK' = 'Middle Keys',
'Sub_regionLK' = 'Lower Keys')Temperature-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$Aragonite_Sat_W
## W = 0.97129, p-value = 1.141e-14
## Model with all data
Temo_all_o<-lmer(Temperature_C~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(Temo_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -1972.7 4011.4
## (1 | SiteID) 1 32 -1972.7 4009.4 0 1 0.9999
## (1 | Month) 0 31 -2983.2 6028.4 2021 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 5.3283 0.6660 8 1168.0 0.4835 0.8685435
## Zone:Sub_region 2 6.4624 0.8078 8 1176.0 0.5885 0.7880553
## Year:Sub_region 3 7.7544 2.5848 3 1184.0 1.8882 0.1297461
## Year:Zone 4 9.4256 3.1419 3 1187.0 2.2900 0.0767345
## Year 0 9.4429 9.4429 1 1193.3 6.8602 0.0089254
## Zone 0 24.4041 8.1347 3 1190.0 5.9098 0.0005303
## Sub_region 0 12.4609 4.1536 3 1190.0 3.0176 0.0289965
##
## Year:Zone:Sub_region
## Zone:Sub_region
## Year:Sub_region
## Year:Zone .
## Year **
## Zone ***
## Sub_region *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Temperature_C ~ Year + Zone + Sub_region + (1 | Month)Temp_all_1<-lmer(Temperature_C ~ Year + Zone + (1 | Month) +
Year:Zone, data=Trends.data)
anova(Temp_all_1, Temo_all_o) summary(Temp_all_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ Year + Zone + (1 | Month) + Year:Zone
## Data: Trends.data
##
## REML criterion at convergence: 3915
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -7.4600 -0.5255 -0.0322 0.4641 3.4157
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 8.590 2.931
## Residual 1.379 1.174
## Number of obs: 1209, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 48.94886 66.81149 1191.80186 0.733 0.4639
## Year -0.01089 0.03311 1191.44747 -0.329 0.7423
## ZoneMid channel -150.34547 87.86814 1190.00171 -1.711 0.0873 .
## ZoneOffshore -196.85780 83.62755 1189.93954 -2.354 0.0187 *
## ZoneOceanic -219.85553 110.22230 1189.96051 -1.995 0.0463 *
## Year:ZoneMid channel 0.07463 0.04355 1190.00171 1.714 0.0868 .
## Year:ZoneOffshore 0.09771 0.04144 1189.93954 2.357 0.0186 *
## Year:ZoneOceanic 0.10917 0.05462 1189.96052 1.998 0.0459 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year ZnMdch ZnOffs ZnOcnc Yr:ZMc Yr:ZnOf
## Year -1.000
## ZoneMdchnnl -0.634 0.634
## ZoneOffshor -0.668 0.668 0.506
## ZoneOceanic -0.510 0.510 0.384 0.404
## Yr:ZnMdchnn 0.634 -0.634 -1.000 -0.506 -0.384
## Yr:ZnOffshr 0.668 -0.668 -0.506 -1.000 -0.404 0.506
## Year:ZnOcnc 0.510 -0.510 -0.384 -0.404 -1.000 0.384 0.404 anova(Temp_all_1) ranova(Temp_all_1)# Obtain slopes
#ranef(Temp_all_1)
#fixef(Temp_all_1)
#coef(Temp_all_1)$Month
#coef(Temp_all_1)$Zone
m.lst <- lstrends(Temp_all_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore -0.0109 0.0331 1191 -0.07585 0.0541
## Mid channel 0.0637 0.0341 1191 -0.00319 0.1307
## Offshore 0.0868 0.0313 1192 0.02536 0.1483
## Oceanic 0.0983 0.0473 1191 0.00549 0.1911
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel -0.0746 0.0435 1190 -1.714 0.3169
## Inshore - Offshore -0.0977 0.0414 1190 -2.357 0.0860
## Inshore - Oceanic -0.1092 0.0546 1190 -1.998 0.1891
## Mid channel - Offshore -0.0231 0.0423 1190 -0.546 0.9476
## Mid channel - Oceanic -0.0345 0.0552 1190 -0.625 0.9240
## Offshore - Oceanic -0.0115 0.0536 1190 -0.214 0.9965
##
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesInshore
# Inshore Omega
hist(Trends.inshore$Temperature_C)
shapiro.test(Trends.inshore$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$Aragonite_Sat_W
## W = 0.98784, p-value = 0.005584 Temp_in_0<-lmer(Temperature_C~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(Omega_in_0)
#summary(Omega_in_1)
#anova(Omega_in_0, Omega_in_1)
#step(Omega_in_1)
anova(Temp_in_0) ranova(Temp_in_0) step(Temp_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -646.07 1314.2
## (1 | SiteID) 1 10 -646.07 1312.2 0.00 1 1
## (1 | Month) 0 9 -920.58 1859.2 549.02 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 7.9739 2.65798 3 326.96 1.3232 0.2667
## Year 2 0.3938 0.39378 1 333.34 0.1955 0.6587
## Sub_region 3 8.5028 2.83425 3 331.00 1.4098 0.2398
##
## Model found:
## Temperature_C ~ (1 | Month) Temp_in<-lmer(Temperature_C ~ (1 | Month),
data=Trends.inshore)
ranova(Temp_in) summary(Temp_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 1283.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.1959 -0.5001 0.0160 0.5502 2.7613
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 12.041 3.47
## Residual 2.018 1.42
## Number of obs: 346, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 26.980 1.005 10.975 26.85 2.32e-11 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(Temp_in)
qqnorm(resid(Temp_in))
acf(resid(Temp_in), main="acf(resid(Temperature Inshore))")
Mid channel
hist(Trends.midchanel$Temperature_C)
shapiro.test(Trends.midchanel$Temperature_C)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$Temperature_C
## W = 0.97295, p-value = 1.028e-05Temp_mc_0<-lmer(Temperature_C~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
anova(Temp_mc_0)ranova(Temp_mc_0)step(Temp_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -505.89 1033.8
## (1 | SiteID) 1 10 -505.89 1031.8 0.00 1 1
## (1 | Month) 0 9 -792.87 1603.7 573.94 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 1.0244 0.3415 3 300.95 0.3126 0.81631
## Sub_region 2 4.1315 1.3772 3 303.98 1.2692 0.28504
## Year 3 3.2289 3.2289 1 309.49 2.9679 0.08593 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Temperature_C ~ (1 | Month)Temp_mc<-lmer(Temperature_C ~ (1 | Month),
data = Trends.midchanel)
summary(Temp_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 998.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0528 -0.5494 -0.0563 0.4968 2.7791
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 8.554 2.925
## Residual 1.096 1.047
## Number of obs: 320, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 27.2397 0.8466 10.9778 32.18 3.24e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(Temp_mc)
qqnorm(resid(Temp_mc))
acf(resid(Temp_mc), main="acf(resid(Omega mid Channel))")
Offshore
hist(Trends.offshore$Temperature_C)
shapiro.test(Trends.offshore$Temperature_C)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$Temperature_C
## W = 0.96684, p-value = 8.65e-08Temp_of_0<-lmer(Temperature_C~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(Omega_of_0)
anova(Temp_of_0)ranova(Temp_of_0)step(Temp_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -490.60 1003.2
## (1 | SiteID) 1 10 -490.60 1001.2 0.00 1 1
## (1 | Month) 0 9 -906.96 1831.9 832.71 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 2.0849 0.6950 3 374.98 1.2340 0.297079
## Sub_region 2 3.1704 1.0568 3 377.98 1.8731 0.133631
## Year 0 4.4221 4.4221 1 382.67 7.7841 0.005535 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Temperature_C ~ Year + (1 | Month)Temp_of<-lmer(Temperature_C ~ (1 | Month),
data =Trends.offshore)
summary(Temp_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 971.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.2993 -0.6222 -0.0863 0.4675 3.2857
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6.7781 2.6035
## Residual 0.5789 0.7608
## Number of obs: 394, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 27.2617 0.7527 10.9783 36.22 8.92e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(Temp_of)
qqnorm(resid(Temp_of))
acf(resid(Temp_of), main="acf(resid(Omega offshore))") 
Oceanic
hist(Trends.ocean$Temperature_C)
shapiro.test(Trends.ocean$Temperature_C)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$Temperature_C
## W = 0.9499, p-value = 3.415e-05Temp_oc_0<-lmer(Temperature_C ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
anova(Temp_oc_0) ranova(Temp_oc_0) step(Temp_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -199.89 417.79
## (1 | SiteID) 1 8 -199.89 415.79 0.00 1 1
## (1 | Month) 0 7 -329.75 673.50 259.71 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.38581 0.19291 2 131.95 0.3520 0.7039
## Sub_region 2 0.09723 0.04861 2 133.93 0.0896 0.9143
## Year 3 0.58385 0.58385 1 137.66 1.0907 0.2981
##
## Model found:
## Temperature_C ~ (1 | Month)Temp_oc<-lmer(Temperature_C ~(1 | Month), data = Trends.ocean)
summary(Temp_oc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Temperature_C ~ (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 385.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2127 -0.5855 -0.1571 0.5743 2.9673
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 5.6880 2.3849
## Residual 0.5368 0.7327
## Number of obs: 149, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 27.3896 0.6915 10.9174 39.61 3.81e-13 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ranova(Temp_oc) plot(Temp_oc)
qqnorm(resid(Temp_oc))
acf(resid(Temp_oc), main="acf(resid(Temp_oc open))")
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.Temp_in_0 <- data.frame (anova(Temp_in_0))
av.Temp_mc_0 <- data.frame (anova(Temp_mc_0) )
av.Temp_of_0 <- data.frame (anova(Temp_of_0))
av.Temp_oc_0 <- data.frame (anova(Temp_oc_0))
# Putting all into one dataframe/table
anova_Temp_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.Temp_in_0, av.Temp_mc_0,
av.Temp_of_0, av.Temp_oc_0)))
colnames(anova_Temp_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_Temp_0) <- NULL
# create HTML table using kableExtra
anova_Temp_0 %>% kable("html", digits=2,
caption="Initial temperature models fixed factors: Year*Region") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| Year | 0.67 | 0.67 | 1 | 330.26 | 0.34 | 0.56 |
| Sub_region | 7.97 | 2.66 | 3 | 326.98 | 1.32 | 0.27 |
| Year:Sub_region | 7.97 | 2.66 | 3 | 326.98 | 1.32 | 0.27 |
| Omega mid-channel | ||||||
| Year | 3.22 | 3.22 | 1 | 303.39 | 2.95 | 0.09 |
| Sub_region | 1.03 | 0.34 | 3 | 300.98 | 0.31 | 0.82 |
| Year:Sub_region | 1.02 | 0.34 | 3 | 300.98 | 0.31 | 0.82 |
| Omega offshore | ||||||
| Year | 5.35 | 5.35 | 1 | 376.55 | 9.51 | 0.00 |
| Sub_region | 2.08 | 0.69 | 3 | 374.95 | 1.23 | 0.30 |
| Year:Sub_region | 2.08 | 0.69 | 3 | 374.95 | 1.23 | 0.30 |
| Omega ocean | ||||||
| Year | 0.32 | 0.32 | 1 | 133.59 | 0.58 | 0.45 |
| Sub_region | 0.39 | 0.19 | 2 | 131.94 | 0.35 | 0.70 |
| Year:Sub_region | 0.39 | 0.19 | 2 | 131.94 | 0.35 | 0.70 |
# R-ANOVAs
rav.Temp_in_0 <- data.frame (ranova(Temp_in_0))
rav.Temp_mc_0 <- data.frame (ranova(Temp_mc_0) )
rav.Temp_of_0 <- data.frame (ranova(Temp_of_0))
rav.Temp_oc_0 <- data.frame (ranova(Temp_oc_0))
# Putting all into one dataframe/table
ranova_Temp_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.Temp_in_0, rav.Temp_mc_0,
rav.Temp_of_0, rav.Temp_oc_0)))
colnames(ranova_Temp_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_Temp_0) <- NULL
# create HTML table using kableExtra
ranova_Temp_0 %>% kable("html", digits=2,
caption="Initial temperature models random factors: Month+Station") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| <none> | 11 | -646.07 | 1314.15 | |||
| (1 | Month) | 10 | -920.58 | 1861.16 | 549.02 | 1 | 0 |
| (1 | SiteID) | 10 | -646.07 | 1312.15 | 0.00 | 1 | 1 |
| Omega mid-channel | ||||||
| <none> | 11 | -505.89 | 1033.79 | |||
| (1 | Month) | 10 | -792.87 | 1605.73 | 573.94 | 1 | 0 |
| (1 | SiteID) | 10 | -505.89 | 1031.79 | 0.00 | 1 | 1 |
| Omega offshore | ||||||
| <none> | 11 | -490.60 | 1003.21 | |||
| (1 | Month) | 10 | -906.96 | 1833.91 | 832.71 | 1 | 0 |
| (1 | SiteID) | 10 | -490.60 | 1001.21 | 0.00 | 1 | 1 |
| Omega ocean | ||||||
| <none> | 9 | -199.89 | 417.79 | |||
| (1 | Month) | 8 | -329.75 | 675.50 | 259.71 | 1 | 0 |
| (1 | SiteID) | 8 | -199.89 | 415.79 | 0.00 | 1 | 1 |
Temp_models_0<-list("Inshore" = Temp_in_0,
"Mid channel" = Temp_mc_0,
"Offshore" = Temp_of_0,
"Ocean" = Temp_oc_0)
modelsummary(Temp_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Initial temperature model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.104 | 0.092 | 0.133** | −0.010 |
| (0.088) | (0.066) | (0.047) | (0.072) | |
| Intercept | −183.974 | −157.964 | −241.064* | 47.800 |
| (176.787) | (132.574) | (94.175) | (144.340) | |
| Upper Keys | 52.495 | 88.854 | 133.838 | |
| (223.284) | (181.158) | (116.203) | ||
| Middle Keys | 185.262 | 13.045 | 134.123 | −102.725 |
| (216.610) | (160.336) | (115.108) | (163.475) | |
| Lower Keys | 382.245+ | 128.562 | 209.830+ | −137.042 |
| (217.219) | (161.197) | (109.249) | (163.871) | |
| Num.Obs. | 346 | 320 | 394 | 149 |
| R2 Marg. | 0.024 | 0.029 | 0.053 | 0.017 |
| R2 Cond. | ||||
| AIC | 1314.1 | 1033.8 | 1003.2 | 417.8 |
| BIC | 1356.5 | 1075.2 | 1046.9 | 444.8 |
| RMSE | 1.38 | 1.01 | 0.73 | 0.70 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Models with non-significant factors removed
Temp_models<-list("Inshore" = Temp_in,
"Mid channel" = Temp_mc,
"Offshore" = Temp_of,
"Ocean" = Temp_oc)
modelsummary(Temp_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Final Omega model outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Intercept | 26.980*** | 27.240*** | 27.262*** | 27.390*** |
| (1.005) | (0.847) | (0.753) | (0.692) | |
| Num.Obs. | 346 | 320 | 394 | 149 |
| R2 Marg. | 0.000 | 0.000 | 0.000 | 0.000 |
| R2 Cond. | 0.856 | 0.886 | 0.921 | 0.914 |
| AIC | 1289.2 | 1004.5 | 977.3 | 391.9 |
| BIC | 1300.8 | 1015.8 | 989.2 | 400.9 |
| ICC | 0.9 | 0.9 | 0.9 | 0.9 |
| RMSE | 1.40 | 1.03 | 0.75 | 0.70 |
| + p |
#modelsummary(Temp_models, estimate = "p.value")DIC-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$DIC_umol_kg
## W = 0.89265, p-value < 2.2e-16
## Model with all data
DIC_all_o<-lmer(DIC_umol_kg~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(DIC_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -6447.7 12962
## (1 | Month) 0 32 -6634.8 13334 374.08 1 < 2e-16 ***
## (1 | SiteID) 0 32 -6449.6 12963 3.65 1 0.05618 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 13180 1647.5 8 1137.17 0.5432 0.82440
## Year:Zone 2 2471 823.5 3 1144.91 0.2724 0.84533
## Year:Sub_region 0 27593 9197.6 3 1147.73 3.0482 0.02783 *
## Zone:Sub_region 0 73938 9242.3 8 24.42 3.0630 0.01557 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Zone + Sub_region + (1 | Month) + (1 | SiteID) + Year:Sub_region + Zone:Sub_regionDIC_all_1<-lmer(DIC_umol_kg ~ Year + Zone + Sub_region +
(1 | Month) + (1 | SiteID) +
Year:Sub_region + Zone:Sub_region, data=Trends.data)
summary(DIC_all_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Zone + Sub_region + (1 | Month) + (1 | SiteID) +
## Year:Sub_region + Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: 12950.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.6604 -0.4911 -0.0099 0.5799 4.7194
##
## Random effects:
## Groups Name Variance Std.Dev.
## SiteID (Intercept) 64.52 8.033
## Month (Intercept) 1524.91 39.050
## Residual 3017.40 54.931
## Number of obs: 1199, groups: SiteID, 38; Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3123.0373 3948.1445 1154.2529 0.791 0.429098
## Year -0.5305 1.9565 1154.2299 -0.271 0.786329
## ZoneMid channel -3.7677 12.4904 22.7085 -0.302 0.765664
## ZoneOffshore -3.2992 12.4617 22.5030 -0.265 0.793612
## ZoneOceanic 63.6729 11.6436 24.6719 5.468 1.16e-05
## Sub_regionUK 3844.5160 4962.5575 1147.9226 0.775 0.438674
## Sub_regionMK -7628.6766 4679.6966 1146.6032 -1.630 0.103341
## Sub_regionLK 2155.8553 4643.0911 1146.8134 0.464 0.642510
## Year:Sub_regionUK -1.9153 2.4594 1147.9150 -0.779 0.436292
## Year:Sub_regionMK 3.7565 2.3191 1146.5903 1.620 0.105551
## Year:Sub_regionLK -1.1025 2.3010 1146.8012 -0.479 0.631944
## ZoneMid channel:Sub_regionUK 10.6945 17.2541 24.5072 0.620 0.541094
## ZoneOffshore:Sub_regionUK 15.4035 16.2952 23.6208 0.945 0.354089
## ZoneOceanic:Sub_regionUK -53.5128 19.2986 27.5410 -2.773 0.009851
## ZoneMid channel:Sub_regionMK 26.2411 16.2049 23.1626 1.619 0.118912
## ZoneOffshore:Sub_regionMK 40.0185 16.1733 22.9838 2.474 0.021161
## ZoneOceanic:Sub_regionMK -18.9579 16.4713 24.7115 -1.151 0.260762
## ZoneMid channel:Sub_regionLK 43.2103 16.1546 22.8690 2.675 0.013568
## ZoneOffshore:Sub_regionLK 65.9258 15.7197 22.7182 4.194 0.000355
##
## (Intercept)
## Year
## ZoneMid channel
## ZoneOffshore
## ZoneOceanic ***
## Sub_regionUK
## Sub_regionMK
## Sub_regionLK
## Year:Sub_regionUK
## Year:Sub_regionMK
## Year:Sub_regionLK
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK
## ZoneOceanic:Sub_regionUK **
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK *
## ZoneOceanic:Sub_regionMK
## ZoneMid channel:Sub_regionLK *
## ZoneOffshore:Sub_regionLK ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(DIC_all_1) ranova(DIC_all_1)# Obtain slopes
# ranef(DIC_all_1)
# fixef(DIC_all_1)
# coef(DIC_all_1)$Month
# coef(DIC_all_1)$Zone
m.lst <- lstrends(DIC_all_1, "Sub_region", var="Year")
m.lst## Sub_region Year.trend SE df lower.CL upper.CL
## BB -0.531 1.96 1154 -4.372 3.31
## UK -2.446 1.73 1159 -5.831 0.94
## MK 3.226 1.51 1157 0.258 6.19
## LK -1.633 1.50 1158 -4.574 1.31
##
## Results are averaged over the levels of: Zone
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 #pairs(m.lst)Inshore
# Inshore DIC
hist(Trends.inshore$DIC_umol_kg)
shapiro.test(Trends.inshore$DIC_umol_kg) # Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$DIC_umol_kg
## W = 0.99743, p-value = 0.8723 DIC_in_0<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
DIC_in_1<-lmer(DIC_umol_kg~Year * Sub_region * Season +
(1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
summary(DIC_in_0)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year * Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3889.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.15665 -0.60937 -0.00018 0.65128 2.88050
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6890.9 83.01
## SiteID (Intercept) 263.8 16.24
## Residual 4793.0 69.23
## Number of obs: 344, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 4.485e+03 8.617e+03 3.218e+02 0.521 0.603
## Year -1.204e+00 4.270e+00 3.217e+02 -0.282 0.778
## Sub_regionUK 2.736e+03 1.091e+04 3.185e+02 0.251 0.802
## Sub_regionMK -1.705e+04 1.058e+04 3.183e+02 -1.611 0.108
## Sub_regionLK 1.546e+03 1.066e+04 3.183e+02 0.145 0.885
## Year:Sub_regionUK -1.364e+00 5.408e+00 3.185e+02 -0.252 0.801
## Year:Sub_regionMK 8.427e+00 5.245e+00 3.183e+02 1.607 0.109
## Year:Sub_regionLK -8.012e-01 5.282e+00 3.183e+02 -0.152 0.880
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK Sb_rLK Y:S_UK Y:S_MK
## Year -1.000
## Sub_reginUK -0.700 0.700
## Sub_reginMK -0.723 0.723 0.569
## Sub_reginLK -0.709 0.709 0.563 0.580
## Yr:Sb_rgnUK 0.700 -0.700 -1.000 -0.569 -0.563
## Yr:Sb_rgnMK 0.723 -0.723 -0.569 -1.000 -0.580 0.569
## Yr:Sb_rgnLK 0.709 -0.709 -0.563 -0.580 -1.000 0.563 0.580 summary(DIC_in_1) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year * Sub_region * Season + (1 | Month) + (1 |
## SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3633.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.15765 -0.59852 -0.01445 0.61366 2.74407
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 639.7 25.29
## SiteID (Intercept) 241.6 15.54
## Residual 4296.7 65.55
## Number of obs: 344, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value
## (Intercept) 4.748e+03 2.063e+04 2.842e+02 0.230
## Year -1.309e+00 1.022e+01 2.843e+02 -0.128
## Sub_regionUK -1.699e+04 2.616e+04 2.990e+02 -0.649
## Sub_regionMK -3.380e+04 2.531e+04 2.971e+02 -1.335
## Sub_regionLK 1.302e+04 2.516e+04 2.973e+02 0.518
## SeasonSpring -2.804e+04 2.532e+04 2.991e+02 -1.108
## SeasonSummer 1.545e+04 2.623e+04 2.966e+02 0.589
## SeasonFall 2.152e+04 2.604e+04 2.783e+02 0.827
## Year:Sub_regionUK 8.415e+00 1.296e+01 2.990e+02 0.649
## Year:Sub_regionMK 1.675e+01 1.254e+01 2.971e+02 1.336
## Year:Sub_regionLK -6.470e+00 1.247e+01 2.973e+02 -0.519
## Year:SeasonSpring 1.383e+01 1.255e+01 2.991e+02 1.102
## Year:SeasonSummer -7.700e+00 1.300e+01 2.966e+02 -0.592
## Year:SeasonFall -1.066e+01 1.290e+01 2.783e+02 -0.826
## Sub_regionUK:SeasonSpring 4.111e+04 3.218e+04 2.985e+02 1.278
## Sub_regionMK:SeasonSpring 5.576e+04 3.149e+04 2.973e+02 1.771
## Sub_regionLK:SeasonSpring -1.409e+04 3.160e+04 2.977e+02 -0.446
## Sub_regionUK:SeasonSummer 1.158e+03 3.309e+04 2.988e+02 0.035
## Sub_regionMK:SeasonSummer 1.537e+04 3.242e+04 2.975e+02 0.474
## Sub_regionLK:SeasonSummer -1.965e+04 3.330e+04 2.996e+02 -0.590
## Sub_regionUK:SeasonFall 3.872e+04 3.368e+04 2.990e+02 1.150
## Sub_regionMK:SeasonFall 5.532e+03 3.189e+04 2.976e+02 0.173
## Sub_regionLK:SeasonFall -5.455e+03 3.161e+04 2.976e+02 -0.173
## Year:Sub_regionUK:SeasonSpring -2.039e+01 1.595e+01 2.985e+02 -1.279
## Year:Sub_regionMK:SeasonSpring -2.767e+01 1.561e+01 2.973e+02 -1.773
## Year:Sub_regionLK:SeasonSpring 6.970e+00 1.566e+01 2.977e+02 0.445
## Year:Sub_regionUK:SeasonSummer -5.765e-01 1.640e+01 2.988e+02 -0.035
## Year:Sub_regionMK:SeasonSummer -7.676e+00 1.607e+01 2.975e+02 -0.478
## Year:Sub_regionLK:SeasonSummer 9.702e+00 1.651e+01 2.996e+02 0.588
## Year:Sub_regionUK:SeasonFall -1.919e+01 1.669e+01 2.990e+02 -1.150
## Year:Sub_regionMK:SeasonFall -2.764e+00 1.580e+01 2.976e+02 -0.175
## Year:Sub_regionLK:SeasonFall 2.673e+00 1.566e+01 2.976e+02 0.171
## Pr(>|t|)
## (Intercept) 0.8182
## Year 0.8982
## Sub_regionUK 0.5167
## Sub_regionMK 0.1829
## Sub_regionLK 0.6051
## SeasonSpring 0.2689
## SeasonSummer 0.5562
## SeasonFall 0.4092
## Year:Sub_regionUK 0.5168
## Year:Sub_regionMK 0.1827
## Year:Sub_regionLK 0.6043
## Year:SeasonSpring 0.2713
## Year:SeasonSummer 0.5540
## Year:SeasonFall 0.4096
## Sub_regionUK:SeasonSpring 0.2023
## Sub_regionMK:SeasonSpring 0.0776 .
## Sub_regionLK:SeasonSpring 0.6561
## Sub_regionUK:SeasonSummer 0.9721
## Sub_regionMK:SeasonSummer 0.6358
## Sub_regionLK:SeasonSummer 0.5556
## Sub_regionUK:SeasonFall 0.2512
## Sub_regionMK:SeasonFall 0.8624
## Sub_regionLK:SeasonFall 0.8631
## Year:Sub_regionUK:SeasonSpring 0.2019
## Year:Sub_regionMK:SeasonSpring 0.0773 .
## Year:Sub_regionLK:SeasonSpring 0.6566
## Year:Sub_regionUK:SeasonSummer 0.9720
## Year:Sub_regionMK:SeasonSummer 0.6332
## Year:Sub_regionLK:SeasonSummer 0.5571
## Year:Sub_regionUK:SeasonFall 0.2512
## Year:Sub_regionMK:SeasonFall 0.8613
## Year:Sub_regionLK:SeasonFall 0.8646
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 anova(DIC_in_0, DIC_in_1) step(DIC_in_1)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 35 -1816.9 3703.9
## (1 | Month) 0 34 -1825.4 3718.9 17.0195 1 3.7e-05 ***
## (1 | SiteID) 0 34 -1819.5 3707.1 5.2456 1 0.022 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region:Season 1 56077 6230.7 9 299.17 1.4501 0.16612
## Year:Sub_region 2 15025 5008.3 3 308.33 1.1508 0.32881
## Year:Season 0 43951 14650.2 3 140.19 3.3603 0.02063
## Sub_region:Season 0 155810 17312.2 9 311.18 3.9709 8.217e-05
##
## Year:Sub_region:Season
## Year:Sub_region
## Year:Season *
## Sub_region:Season ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Sub_region + Season + (1 | Month) + (1 | SiteID) + Year:Season + Sub_region:Season anova(DIC_in_1) ranova(DIC_in_1) step(DIC_in_1)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 35 -1816.9 3703.9
## (1 | Month) 0 34 -1825.4 3718.9 17.0195 1 3.7e-05 ***
## (1 | SiteID) 0 34 -1819.5 3707.1 5.2456 1 0.022 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region:Season 1 56077 6230.7 9 299.17 1.4501 0.16612
## Year:Sub_region 2 15025 5008.3 3 308.33 1.1508 0.32881
## Year:Season 0 43951 14650.2 3 140.19 3.3603 0.02063
## Sub_region:Season 0 155810 17312.2 9 311.18 3.9709 8.217e-05
##
## Year:Sub_region:Season
## Year:Sub_region
## Year:Season *
## Sub_region:Season ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + Sub_region + Season + (1 | Month) + (1 | SiteID) + Year:Season + Sub_region:Season DIC_in<-lmer(DIC_umol_kg ~ Year + Sub_region +
(1 | Month) + (1 | SiteID),
data=Trends.inshore)
anova(DIC_in) summary(DIC_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3909.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4187 -0.6488 -0.0022 0.6496 2.7936
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6878.9 82.94
## SiteID (Intercept) 261.7 16.18
## Residual 4829.2 69.49
## Number of obs: 344, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 675.9450 4678.1870 331.5171 0.144 0.88520
## Year 0.6833 2.3182 331.5036 0.295 0.76838
## Sub_regionUK -16.4631 18.5970 7.1538 -0.885 0.40479
## Sub_regionMK -46.2885 18.5087 7.0228 -2.501 0.04083 *
## Sub_regionLK -70.7519 18.4783 6.9754 -3.829 0.00651 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.026 0.024
## Sub_reginMK -0.013 0.010 0.590
## Sub_reginLK -0.023 0.021 0.591 0.595 plot(DIC_in)
qqnorm(resid(DIC_in))
acf(resid(DIC_in), main="acf(resid(DIC Inshore))")
DIC_in.emmc<-emmeans(DIC_in, ~Sub_region)
DIC_in.emmc<-multcomp::cld(DIC_in.emmc)
#DIC_in.emmc<-as.data.frame(DIC_in.emmc[complete.cases(DIC_in.emmc),])
DIC_in.emmc<-DIC_in.emmc[order(DIC_in.emmc$Sub_region),]
DIC_in.emmc #write.csv(Temp_in.emmc, "FLK_results/Model_9_LTpH_POST.csv")
round(DIC_in.emmc$emmean, digits = 2)## [1] 2054.61 2038.15 2008.33 1983.86 #anova(DIC_in)
#ranova(DIC_in)# Create a new data frame for independent variables
In_Newdata<-expand.grid(Sub_region=unique(Trends.inshore$Sub_region),
Year=seq((min(Trends.inshore$Year)-0),
(max(Trends.inshore$Year)+1)))
In_DIC_Pred <- predict(DIC_in, newdata=In_Newdata,
re.form=NA, level=0, asList = FALSE)
In_DIC_Pred <-cbind(In_Newdata, In_DIC_Pred)
In_DIC_Pred$Date<-as.Date(as.character(In_DIC_Pred$Year), format = "%Y")
# Plot raw data point and fitted model
In_DIC_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(0,6),
name=(expression(~DIC[Ar])),
#limits = c(2.3, 5.2),
#breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=DIC_umol_kg, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=21)+
geom_line(data=In_DIC_Pred, aes(y = In_DIC_Pred,
x=Date, colour = Sub_region), linetype=2, size=1)
In_DIC_Modeled
Mid channel
hist(Trends.midchanel$DIC_umol_kg)
shapiro.test(Trends.midchanel$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$DIC_umol_kg
## W = 0.93354, p-value = 1.006e-10DIC_mc_0<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
# DIC_mc_1<-lmer(DIC_umol_kg~Year * Sub_region * Season +
# #(1|Zone)+
# (1|SiteID), data=Trends.midchanel)
#summary(DIC_mc_0)
#summary(DIC_mc_1)
anova(DIC_mc_0)#anova(DIC_mc_1)
#anova(DIC_mc_0, DIC_mc_1)
ranova(DIC_mc_0)step(DIC_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1658.0 3338.0
## (1 | SiteID) 1 10 -1658.7 3337.3 1.392 1 0.2381
## (1 | Month) 0 9 -1723.8 3465.6 130.301 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 8969.4 2989.8 3 298.83 1.4612 0.225205
## Year 2 2116.7 2116.7 1 312.94 1.0294 0.311081
## Sub_region 0 29687.4 9895.8 3 303.09 4.8018 0.002777 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month)DIC_mc<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(DIC_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3334
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3673 -0.4419 0.0603 0.5355 3.9086
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1701 41.24
## Residual 2056 45.35
## Number of obs: 318, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5217.407 3124.726 312.935 1.670 0.095975 .
## Year -1.571 1.548 312.936 -1.015 0.311081
## Sub_regionUK -8.845 8.252 301.917 -1.072 0.284641
## Sub_regionMK -21.745 7.279 301.853 -2.987 0.003045 **
## Sub_regionLK -24.605 7.267 301.979 -3.386 0.000803 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.019 0.018
## Sub_reginMK -0.004 0.003 0.528
## Sub_reginLK -0.025 0.024 0.531 0.602 anova(DIC_mc) plot(DIC_mc)
qqnorm(resid(DIC_mc))
acf(resid(DIC_mc), main="acf(resid(DIC mid Channel))") 
Temp_md.emmc<-emmeans(DIC_mc, ~Sub_region+Year)
Temp_md.emmc<-multcomp::cld(Temp_md.emmc)
#Temp_in.emmc<-as.data.frame(Temp_in.emmc[complete.cases(Temp_in.emmc),])
Temp_md.emmc<-Temp_md.emmc[order(Temp_md.emmc$Sub_region),]
Temp_md.emmc #write.csv(Temp_md.emmc, "FLK_results/Model_10_LTpH_POST.csv")# Create a new data frame for independent variables
Mc_Newdata<-expand.grid(Zone=unique(Trends.midchanel$Zone),
Sub_region=unique(Trends.midchanel$Sub_region),
Year=seq((min(Trends.midchanel$Year)-0), (max(Trends.midchanel$Year)+1)))
Mc_DIC_Pred <- predict(DIC_mc, newdata=Mc_Newdata, re.form=NA, level=0, asList = FALSE)
Mc_DIC_Pred <-cbind(Mc_Newdata, Mc_DIC_Pred)
Mc_DIC_Pred$Date<-as.Date(as.character(Mc_DIC_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(
summary(DIC_mc)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.midchanel$DIC_umol_kg, na.rm =T)+ 0.3
# Plot raw data point and fitted model
plot_label <- sprintf("\"\" ~ DIC[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
Mc_DIC_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(DIC[Ar]))),
name=(expression(~DIC[Ar])),
limits = c(2.3, 5.2),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_jitter(data=Trends.midchanel,
aes(y=DIC_umol_kg, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_DIC_Pred, aes(y = Mc_DIC_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
# geom_text(data = slope, aes(x = xs, y = ys,
# label = (paste("~DIC[Ar]", "=",
# slope, "Year ^-1"))), size=10)
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~DIC[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_DIC_Modeled
Offshore
hist(Trends.offshore$DIC_umol_kg)
shapiro.test(Trends.offshore$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$DIC_umol_kg
## W = 0.92737, p-value = 8.011e-13DIC_of_0<-lmer(DIC_umol_kg~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(DIC_of_0)
anova(DIC_of_0)ranova(DIC_of_0)step(DIC_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1695.8 3413.7
## (1 | SiteID) 1 10 -1695.8 3411.7 0.00 1 1
## (1 | Month) 0 9 -1757.9 3533.8 124.11 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 878.8 292.95 3 370.59 0.8509 0.466791
## Year 2 406.7 406.66 1 384.15 1.1825 0.277525
## Sub_region 0 4526.5 1508.84 3 374.98 4.3971 0.004676 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month)DIC_of<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(DIC_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3400.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8843 -0.4329 0.1397 0.6130 2.4102
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 243.8 15.61
## Residual 343.9 18.54
## Number of obs: 390, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 792.3190 1154.5069 384.1392 0.686 0.49295
## Year 0.6221 0.5721 384.1451 1.087 0.27753
## Sub_regionUK -2.9722 2.9870 373.5600 -0.995 0.32037
## Sub_regionMK -9.1133 2.9547 373.5576 -3.084 0.00219 **
## Sub_regionLK -1.3305 2.8028 373.7602 -0.475 0.63528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.007 0.006
## Sub_reginMK -0.007 0.005 0.590
## Sub_reginLK -0.020 0.019 0.624 0.632 anova(DIC_of) plot(DIC_of)
qqnorm(resid(DIC_of))
acf(resid(DIC_of), main="acf(resid(DIC offshore))") 
Temp_of.emmc<-emmeans(DIC_of, ~Sub_region+Year)
Temp_of.emmc<-multcomp::cld(Temp_of.emmc)
#Temp_in.emmc<-as.data.frame(Temp_in.emmc[complete.cases(Temp_in.emmc),])
Temp_of.emmc<-Temp_of.emmc[order(Temp_of.emmc$Sub_region),]
Temp_of.emmc#write.csv(Temp_of.emmc, "FLK_results/Model_11_LTpH_POST.csv")# Create a new data frame for independent variables
Of_Newdata<-expand.grid(one=unique(Trends.offshore$Zone),
Sub_region=unique(Trends.offshore$Sub_region),
Year=seq((min(Trends.offshore$Year)-0), (max(Trends.offshore$Year)+1)))
Of_DIC_Pred <- predict(DIC_of, newdata=Of_Newdata, re.form=NA, level=0, asList = FALSE)
Of_DIC_Pred <-cbind(Of_Newdata, Of_DIC_Pred)
Of_DIC_Pred$Date<-as.Date(as.character(Of_DIC_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(DIC_of)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.offshore$DIC_umol_kg, na.rm =T)
plot_label <- sprintf("\"\" ~ DIC[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_DIC_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(DIC[Ar]))),
name=(expression(~DIC[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.offshore,
aes(y=DIC_umol_kg, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_DIC_Pred, aes(y = Of_DIC_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~DIC[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_DIC_Modeled
Oceanic
hist(Trends.ocean$DIC_umol_kg)
shapiro.test(Trends.ocean$DIC_umol_kg)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$DIC_umol_kg
## W = 0.98641, p-value = 0.1591DIC_oc_0<-lmer(DIC_umol_kg ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
DIC_oc_1<-lmer(DIC_umol_kg ~ Year * Sub_region * Season++ (1|SiteID),
data=Trends.ocean)
#summary(DIC_oc_0)
#summary(DIC_oc_1)
#anova(DIC_oc_1)
anova(DIC_oc_0) ranova(DIC_oc_0) step(DIC_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -579.29 1176.6
## (1 | SiteID) 1 8 -579.29 1174.6 0.000 1 1
## (1 | Month) 0 7 -599.53 1213.1 40.494 1 1.972e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 166.40 83.20 2 129.53 0.5593 0.5730028
## Sub_region 2 121.96 60.98 2 131.46 0.4125 0.6628785
## Year 0 1859.81 1859.81 1 144.81 12.6915 0.0004977 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + (1 | Month)DIC_oc<-lmer(DIC_umol_kg ~ Year + (1 | Month), data = Trends.ocean)
summary(DIC_oc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1171.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4190 -0.4733 0.1114 0.6111 2.2162
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 125.5 11.20
## Residual 146.5 12.11
## Number of obs: 147, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2168.1995 1182.8873 144.8121 -1.833 0.068860 .
## Year 2.0884 0.5862 144.8109 3.563 0.000498 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(DIC_oc) plot(DIC_oc)
qqnorm(resid(DIC_oc))
acf(resid(DIC_oc), main="acf(resid(DIC open))")
Temp_oc.emmc<-emmeans(DIC_oc, ~Year)
#Temp_oc.emmc<-multcomp::cld(Temp_oc.emmc)
#Temp_oc.emmc<-as.data.frame(Temp_in.emmc[complete.cases(Temp_in.emmc),])
#Temp_oc.emmc<-Temp_oc.emmc[order(Temp_oc.emmc$Sub_region),]
#Temp_oc.emmc# Create a new data frame for independent variables
Oc_Newdata<-expand.grid(one=unique(Trends.ocean$Zone),
Sub_region=unique(Trends.ocean$Sub_region),
Year=seq((min(Trends.ocean$Year)-0), (max(Trends.ocean$Year)+1)))
Oc_DIC_Pred <- predict(DIC_oc, newdata=Oc_Newdata, re.form=NA, level=0, asList = FALSE)
Oc_DIC_Pred <-cbind(Oc_Newdata, Oc_DIC_Pred)
Oc_DIC_Pred$Date<-as.Date(as.character(Oc_DIC_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(DIC_oc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
#ys <- max(Trends.inshore$DIC_umol_kg, na.rm =T)
ys <- max(Trends.offshore$DIC_umol_kg, na.rm =T)
plot_label <- sprintf("\"\" ~ DIC[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_DIC_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(DIC[Ar]))),
name=(expression(~DIC[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.ocean,
aes(y=DIC_umol_kg, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_DIC_Pred, aes(y = Oc_DIC_Pred,
x=Date), linetype=1, size=1, colour = "black") +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~DIC[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_DIC_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.DIC_in_0 <- data.frame (anova(DIC_in_0))
av.DIC_mc_0 <- data.frame (anova(DIC_mc_0) )
av.DIC_of_0 <- data.frame (anova(DIC_of_0))
av.DIC_oc_0 <- data.frame (anova(DIC_oc_0))
# Putting all into one dataframe/table
anova_DIC_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.DIC_in_0, av.DIC_mc_0,
av.DIC_of_0, av.DIC_oc_0)))
colnames(anova_DIC_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_DIC_0) <- NULL
# create HTML table using kableExtra
anova_DIC_0 %>% kable("html", digits=2,
caption="Initial DIC models fixed factors: Year*Region") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "DIC inshore", 1, 3) %>% # groups rows with label
pack_rows(., "DIC mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "DIC offshore", 7, 9) %>% # groups rows with label
pack_rows(., "DIC ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| DIC inshore | ||||||
| Year | 115.67 | 115.67 | 1 | 328.24 | 0.02 | 0.88 |
| Sub_region | 25873.54 | 8624.51 | 3 | 318.54 | 1.80 | 0.15 |
| Year:Sub_region | 25839.52 | 8613.17 | 3 | 318.54 | 1.80 | 0.15 |
| DIC mid-channel | ||||||
| Year | 2645.10 | 2645.10 | 1 | 305.07 | 1.31 | 0.25 |
| Sub_region | 9048.05 | 3016.02 | 3 | 293.56 | 1.50 | 0.21 |
| Year:Sub_region | 9047.72 | 3015.91 | 3 | 293.56 | 1.50 | 0.21 |
| DIC offshore | ||||||
| Year | 435.49 | 435.49 | 1 | 381.47 | 1.26 | 0.26 |
| Sub_region | 882.69 | 294.23 | 3 | 370.58 | 0.85 | 0.46 |
| Year:Sub_region | 878.84 | 292.95 | 3 | 370.58 | 0.85 | 0.47 |
| DIC ocean | ||||||
| Year | 1746.29 | 1746.29 | 1 | 140.60 | 11.74 | 0.00 |
| Sub_region | 166.54 | 83.27 | 2 | 129.54 | 0.56 | 0.57 |
| Year:Sub_region | 166.40 | 83.20 | 2 | 129.54 | 0.56 | 0.57 |
# R-ANOVAs
rav.DIC_in_0 <- data.frame (ranova(DIC_in_0))
rav.DIC_mc_0 <- data.frame (ranova(DIC_mc_0) )
rav.DIC_of_0 <- data.frame (ranova(DIC_of_0))
rav.DIC_oc_0 <- data.frame (ranova(DIC_oc_0))
# Putting all into one dataframe/table
ranova_DIC_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.DIC_in_0, rav.DIC_mc_0,
rav.DIC_of_0, rav.DIC_oc_0)))
colnames(ranova_DIC_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_DIC_0) <- NULL
# create HTML table using kableExtra
ranova_DIC_0 %>% kable("html", digits=2,
caption="Initial DIC models random factors: Month+Station") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "DIC inshore", 1, 3) %>% # groups rows with label
pack_rows(., "DIC mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "DIC offshore", 7, 9) %>% # groups rows with label
pack_rows(., "DIC ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| DIC inshore | ||||||
| <none> | 11 | -1944.80 | 3911.60 | |||
| (1 | Month) | 10 | -2065.72 | 4151.43 | 241.83 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1947.36 | 3914.71 | 5.11 | 1 | 0.02 |
| DIC mid-channel | ||||||
| <none> | 11 | -1657.98 | 3337.96 | |||
| (1 | Month) | 10 | -1723.77 | 3467.54 | 131.58 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1658.67 | 3337.35 | 1.39 | 1 | 0.24 |
| DIC offshore | ||||||
| <none> | 11 | -1695.84 | 3413.68 | |||
| (1 | Month) | 10 | -1757.89 | 3535.78 | 124.10 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1695.84 | 3411.68 | 0.00 | 1 | 1.00 |
| DIC ocean | ||||||
| <none> | 9 | -579.29 | 1176.57 | |||
| (1 | Month) | 8 | -599.53 | 1215.07 | 40.49 | 1 | 0.00 |
| (1 | SiteID) | 8 | -579.29 | 1174.57 | 0.00 | 1 | 1.00 |
DIC_models_0<-list("Inshore" = DIC_in_0,
"Mid channel" = DIC_mc_0,
"Offshore" = DIC_of_0,
"Ocean" = DIC_oc_0)
modelsummary(DIC_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Initial DIC model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −1.204 | −1.704 | 0.411 | 2.458* |
| (4.270) | (2.806) | (1.149) | (1.166) | |
| Intercept | 4485.249 | 5485.277 | 1218.372 | −2916.483 |
| (8616.533) | (5661.336) | (2318.160) | (2352.383) | |
| Upper Keys | 2735.858 | 1995.060 | −317.924 | |
| (10911.264) | (7778.521) | (2873.140) | ||
| Middle Keys | −17050.458 | −7159.241 | −2728.834 | −40.890 |
| (10582.937) | (6881.067) | (2846.080) | (2689.779) | |
| Lower Keys | 1545.619 | 5775.656 | 1062.283 | 2062.856 |
| (10658.465) | (6948.171) | (2711.348) | (2708.649) | |
| Num.Obs. | 344 | 318 | 390 | 147 |
| R2 Marg. | 0.062 | 0.035 | 0.043 | 0.126 |
| R2 Cond. | 0.624 | 0.487 | ||
| AIC | 3911.6 | 3338.0 | 3413.7 | 1176.6 |
| BIC | 3953.9 | 3379.3 | 3457.3 | 1203.5 |
| ICC | 0.6 | 0.5 | ||
| RMSE | 66.87 | 43.34 | 18.11 | 11.52 |
| + p |
#modelsummary(DIC_models, estimate = "p.value")Models with non-significant factors removed
DIC_models<-list("Inshore" = DIC_in,
"Mid channel" = DIC_mc,
"Offshore" = DIC_of,
"Ocean" = DIC_oc)
modelsummary(DIC_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Final DIC model outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.683 | −1.571 | 0.622 | 2.088*** |
| (2.318) | (1.548) | (0.572) | (0.586) | |
| Intercept | 675.945 | 5217.407+ | 792.319 | −2168.199+ |
| (4678.187) | (3124.726) | (1154.507) | (1182.887) | |
| Upper Keys | −16.463 | −8.845 | −2.972 | |
| (18.597) | (8.252) | (2.987) | ||
| Middle Keys | −46.289* | −21.745** | −9.113** | |
| (18.509) | (7.279) | (2.955) | ||
| Lower Keys | −70.752*** | −24.605*** | −1.330 | |
| (18.478) | (7.267) | (2.803) | ||
| Num.Obs. | 344 | 318 | 390 | 147 |
| R2 Marg. | 0.057 | 0.027 | 0.022 | 0.067 |
| R2 Cond. | 0.619 | 0.468 | 0.428 | 0.498 |
| AIC | 3925.6 | 3348.0 | 3414.6 | 1179.9 |
| BIC | 3956.3 | 3374.4 | 3442.4 | 1191.9 |
| ICC | 0.6 | 0.5 | 0.4 | 0.5 |
| RMSE | 67.43 | 44.23 | 18.17 | 11.61 |
| + p |
modelsummary(DIC_models, output = "FLK_results/Figures/Fig_2/Trend_DIC.docx")
#modelsummary(DIC_models, estimate = "p.value")Trends_DIC<-ggarrange(In_DIC_Modeled+ theme(
#axis.text.x=element_blank(),
legend.position = "none"),
Mc_DIC_Modeled+ theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Of_DIC_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Oc_DIC_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
labels = c("C"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_DIC
Omega-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$Aragonite_Sat_W
## W = 0.97129, p-value = 1.141e-14
## Model with all data
Omega_all_o<-lmer(Aragonite_Sat_W~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(Omega_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -275.96 617.91
## (1 | SiteID) 1 32 -276.49 616.97 1.059 1 0.3035
## (1 | Month) 0 31 -432.83 927.66 312.693 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 0.1748 0.02185 8 1156.1 0.2736 0.97455
## Year:Sub_region 2 0.0829 0.02764 3 1164.2 0.3478 0.79074
## Year:Zone 0 0.8539 0.28463 3 1167.1 3.5882 0.01334
## Zone:Sub_region 0 5.8363 0.72953 8 1167.1 9.1967 2.436e-12
##
## Year:Zone:Sub_region
## Year:Sub_region
## Year:Zone *
## Zone:Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone + Zone:Sub_regionOmega_all_1<-lmer(Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
Omega_all_3<-lmer(Aragonite_Sat_W ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(Omega_all_1, Omega_all_3) summary(Omega_all_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: 490
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.7230 -0.5939 0.0796 0.5686 5.2083
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.02962 0.1721
## Residual 0.07933 0.2816
## Number of obs: 1197, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2.821e+01 1.601e+01 1.178e+03 -1.762 0.07836
## Year 1.579e-02 7.935e-03 1.178e+03 1.990 0.04680
## ZoneMid channel 1.954e+01 2.116e+01 1.167e+03 0.923 0.35598
## ZoneOffshore 5.313e+01 2.015e+01 1.167e+03 2.636 0.00849
## ZoneOceanic 7.107e+01 2.661e+01 1.167e+03 2.670 0.00768
## Sub_regionUK 2.157e-01 4.573e-02 1.167e+03 4.715 2.70e-06
## Sub_regionMK 9.529e-02 4.516e-02 1.167e+03 2.110 0.03506
## Sub_regionLK -2.871e-01 4.489e-02 1.167e+03 -6.394 2.33e-10
## Year:ZoneMid channel -9.644e-03 1.049e-02 1.167e+03 -0.920 0.35799
## Year:ZoneOffshore -2.627e-02 9.986e-03 1.167e+03 -2.631 0.00863
## Year:ZoneOceanic -3.501e-02 1.319e-02 1.167e+03 -2.655 0.00805
## ZoneMid channel:Sub_regionUK -7.882e-02 6.864e-02 1.167e+03 -1.148 0.25107
## ZoneOffshore:Sub_regionUK -1.749e-01 6.441e-02 1.167e+03 -2.716 0.00671
## ZoneOceanic:Sub_regionUK -4.250e-01 7.820e-02 1.167e+03 -5.434 6.69e-08
## ZoneMid channel:Sub_regionMK -3.654e-02 6.384e-02 1.167e+03 -0.572 0.56719
## ZoneOffshore:Sub_regionMK -7.284e-02 6.363e-02 1.167e+03 -1.145 0.25259
## ZoneOceanic:Sub_regionMK -3.441e-01 6.560e-02 1.167e+03 -5.245 1.86e-07
## ZoneMid channel:Sub_regionLK 1.859e-01 6.351e-02 1.167e+03 2.928 0.00348
## ZoneOffshore:Sub_regionLK 2.458e-01 6.180e-02 1.167e+03 3.977 7.41e-05
##
## (Intercept) .
## Year *
## ZoneMid channel
## ZoneOffshore **
## ZoneOceanic **
## Sub_regionUK ***
## Sub_regionMK *
## Sub_regionLK ***
## Year:ZoneMid channel
## Year:ZoneOffshore **
## Year:ZoneOceanic **
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK **
## ZoneOceanic:Sub_regionUK ***
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK
## ZoneOceanic:Sub_regionMK ***
## ZoneMid channel:Sub_regionLK **
## ZoneOffshore:Sub_regionLK ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(Omega_all_1) ranova(Omega_all_1)# Obtain slopes
#ranef(Omega_all_1)
#fixef(Omega_all_1)
#coef(Omega_all_1)$Month
#coef(Omega_all_1)$Zone
m.lst <- lstrends(Omega_all_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore 0.01579 0.00795 1178 0.000197 0.03138
## Mid channel 0.00615 0.00819 1178 -0.009914 0.02221
## Offshore -0.01048 0.00752 1178 -0.025226 0.00427
## Oceanic -0.01922 0.01141 1174 -0.041605 0.00316
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel 0.00964 0.01049 1167 0.920 0.7944
## Inshore - Offshore 0.02627 0.00999 1167 2.631 0.0428
## Inshore - Oceanic 0.03501 0.01319 1167 2.655 0.0401
## Mid channel - Offshore 0.01663 0.01018 1167 1.633 0.3602
## Mid channel - Oceanic 0.02537 0.01333 1167 1.903 0.2275
## Offshore - Oceanic 0.00874 0.01294 1167 0.675 0.9064
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesInshore
# Inshore Omega
hist(Trends.inshore$Aragonite_Sat_W)
shapiro.test(Trends.inshore$Aragonite_Sat_W) # Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$Aragonite_Sat_W
## W = 0.98784, p-value = 0.005584 Omega_in_0<-lmer(Aragonite_Sat_W~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
Omega_in_1<-lmer(Aragonite_Sat_W~Year * Sub_region * Season +
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(Omega_in_0)
#summary(Omega_in_1)
#anova(Omega_in_0, Omega_in_1)
#step(Omega_in_1)
anova(Omega_in_0) ranova(Omega_in_0) step(Omega_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -146.35 314.69
## (1 | Month) 0 10 -264.88 549.77 237.08 1 < 2e-16 ***
## (1 | SiteID) 0 10 -147.81 315.62 2.93 1 0.08695 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.0379 0.01263 3 318.47 0.1166 0.950349
## Year 2 0.0016 0.00164 1 331.77 0.0153 0.901788
## Sub_region 0 5.1900 1.73001 3 6.95 16.1496 0.001627 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Sub_region + (1 | Month) + (1 | SiteID) Omega_in<-lmer(Aragonite_Sat_W ~ Sub_region + (1 | Month),
data=Trends.inshore)
anova(Omega_in) summary(Omega_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 271.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8965 -0.5776 0.0404 0.5861 4.0803
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.1377 0.3711
## Residual 0.1100 0.3317
## Number of obs: 344, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 3.65753 0.11476 13.72116 31.872 2.9e-14 ***
## Sub_regionUK 0.20061 0.05388 329.15463 3.723 0.000231 ***
## Sub_regionMK 0.09233 0.05325 329.15902 1.734 0.083875 .
## Sub_regionLK -0.28142 0.05297 329.19192 -5.313 2.0e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.267
## Sub_reginMK -0.271 0.573
## Sub_reginLK -0.272 0.574 0.586 plot(Omega_in)
qqnorm(resid(Omega_in))
acf(resid(Omega_in), main="acf(resid(Omega Inshore))")
Arg_in.emmc<-emmeans(Omega_in, ~Sub_region)
Arg_in.emmc<-multcomp::cld(Arg_in.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_in.emmc<-Arg_in.emmc[order(Arg_in.emmc$Sub_region),]
Arg_in.emmc #write.csv(Arg_in.emmc, "FLK_results/Model_9_LTpH_POST.csv")
round(Arg_in.emmc$emmean, digits = 2)## [1] 3.66 3.86 3.75 3.38 #anova(Omega_in)
#ranova(Omega_in)# Create a new data frame for independent variables
In_Newdata<-expand.grid(Zone=unique(Trends.inshore$Zone),
Sub_region=unique(Trends.inshore$Sub_region),
Year=seq((min(Trends.inshore$Year)-0), (max(Trends.inshore$Year)+1)))
In_Omega_Pred <- predict(Omega_in, newdata=In_Newdata, re.form=NA, level=0, asList = FALSE)
In_Omega_Pred <-cbind(In_Newdata, In_Omega_Pred)
In_Omega_Pred$Date<-as.Date(as.character(In_Omega_Pred$Year), format = "%Y")
# Plot raw data point and fitted model
In_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(0,6),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(2.3, 5.2),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=21)+
geom_line(data=In_Omega_Pred, aes(y = In_Omega_Pred,
x=Date, colour = Sub_region), linetype=2, size=1)
In_Omega_Modeled
Mid channel
hist(Trends.midchanel$Aragonite_Sat_W)
shapiro.test(Trends.midchanel$Aragonite_Sat_W)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$Aragonite_Sat_W
## W = 0.97844, p-value = 0.0001033Omega_mc_0<-lmer(Aragonite_Sat_W~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
# Omega_mc_1<-lmer(Aragonite_Sat_W~Year * Sub_region * Season +
# #(1|Zone)+
# (1|SiteID), data=Trends.midchanel)
#summary(Omega_mc_0)
#summary(Omega_mc_1)
anova(Omega_mc_0)#anova(Omega_mc_1)
#anova(Omega_mc_0, Omega_mc_1)
ranova(Omega_mc_0)step(Omega_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -15.198 52.395
## (1 | SiteID) 1 10 -16.127 52.255 1.859 1 0.1727
## (1 | Month) 0 9 -79.135 176.269 126.015 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.05438 0.01813 3 299.17 0.3525 0.7873
## Year 2 0.02606 0.02606 1 312.46 0.5102 0.4756
## Sub_region 0 2.38338 0.79446 3 303.20 15.5664 1.923e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Sub_region + (1 | Month)Omega_mc<-lmer(Aragonite_Sat_W ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(Omega_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 13.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.4729 -0.5930 0.0842 0.6008 3.5456
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.03657 0.1912
## Residual 0.05109 0.2260
## Number of obs: 318, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -7.359615 15.532368 312.454982 -0.474 0.635956
## Year 0.005498 0.007697 312.458538 0.714 0.475605
## Sub_regionUK 0.139603 0.041133 302.227159 3.394 0.000781 ***
## Sub_regionMK 0.058395 0.036283 302.155015 1.609 0.108564
## Sub_regionLK -0.100180 0.036221 302.296169 -2.766 0.006028 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.019 0.018
## Sub_reginMK -0.004 0.003 0.528
## Sub_reginLK -0.025 0.024 0.531 0.602 anova(Omega_mc) plot(Omega_mc)
qqnorm(resid(Omega_mc))
acf(resid(Omega_mc), main="acf(resid(Omega mid Channel))") 
Arg_md.emmc<-emmeans(Omega_mc, ~Sub_region+Year)
Arg_md.emmc<-multcomp::cld(Arg_md.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_md.emmc<-Arg_md.emmc[order(Arg_md.emmc$Sub_region),]
Arg_md.emmc #write.csv(Arg_md.emmc, "FLK_results/Model_10_LTpH_POST.csv")# Create a new data frame for independent variables
Mc_Newdata<-expand.grid(Zone=unique(Trends.midchanel$Zone),
Sub_region=unique(Trends.midchanel$Sub_region),
Year=seq((min(Trends.midchanel$Year)-0), (max(Trends.midchanel$Year)+1)))
Mc_Omega_Pred <- predict(Omega_mc, newdata=Mc_Newdata, re.form=NA, level=0, asList = FALSE)
Mc_Omega_Pred <-cbind(Mc_Newdata, Mc_Omega_Pred)
Mc_Omega_Pred$Date<-as.Date(as.character(Mc_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(
summary(Omega_mc)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.midchanel$Aragonite_Sat_W, na.rm =T)+ 0.3
# Plot raw data point and fitted model
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
Mc_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(2.3, 5.2),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_jitter(data=Trends.midchanel,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_Omega_Pred, aes(y = Mc_Omega_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
# geom_text(data = slope, aes(x = xs, y = ys,
# label = (paste("~Omega[Ar]", "=",
# slope, "Year ^-1"))), size=10)
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_Omega_Modeled
Offshore
hist(Trends.offshore$Aragonite_Sat_W)
shapiro.test(Trends.offshore$Aragonite_Sat_W)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$Aragonite_Sat_W
## W = 0.99379, p-value = 0.1136Omega_of_0<-lmer(Aragonite_Sat_W~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(Omega_of_0)
anova(Omega_of_0)ranova(Omega_of_0)step(Omega_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 208.49 -394.97
## (1 | SiteID) 1 10 207.77 -395.53 1.438 1 0.2305
## (1 | Month) 0 9 112.36 -206.72 190.817 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.06605 0.022018 3 368.71 1.3883 0.2460
## Year 2 0.02010 0.020105 1 382.74 1.2636 0.2617
## Sub_region 0 0.46970 0.156565 3 372.88 9.8508 2.883e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Sub_region + (1 | Month)Omega_of<-lmer(Aragonite_Sat_W ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(Omega_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: -434.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.9928 -0.5880 0.0290 0.6318 3.7875
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.01749 0.1322
## Residual 0.01591 0.1261
## Number of obs: 388, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 12.654489 7.902638 382.739787 1.601 0.11014
## Year -0.004402 0.003916 382.735971 -1.124 0.26167
## Sub_regionUK 0.030178 0.020318 371.700710 1.485 0.13831
## Sub_regionMK 0.023864 0.020098 371.699196 1.187 0.23584
## Sub_regionLK -0.051178 0.019121 371.848012 -2.677 0.00777 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.008 0.006
## Sub_reginMK -0.007 0.006 0.590
## Sub_reginLK -0.021 0.019 0.622 0.630 anova(Omega_of) plot(Omega_of)
qqnorm(resid(Omega_of))
acf(resid(Omega_of), main="acf(resid(Omega offshore))") 
Arg_of.emmc<-emmeans(Omega_of, ~Sub_region+Year)
Arg_of.emmc<-multcomp::cld(Arg_of.emmc)
#Arg_in.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
Arg_of.emmc<-Arg_of.emmc[order(Arg_of.emmc$Sub_region),]
Arg_of.emmc#write.csv(Arg_of.emmc, "FLK_results/Model_11_LTpH_POST.csv")# Create a new data frame for independent variables
Of_Newdata<-expand.grid(one=unique(Trends.offshore$Zone),
Sub_region=unique(Trends.offshore$Sub_region),
Year=seq((min(Trends.offshore$Year)-0), (max(Trends.offshore$Year)+1)))
Of_Omega_Pred <- predict(Omega_of, newdata=Of_Newdata, re.form=NA, level=0, asList = FALSE)
Of_Omega_Pred <-cbind(Of_Newdata, Of_Omega_Pred)
Of_Omega_Pred$Date<-as.Date(as.character(Of_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(Omega_of)$coefficients["Year", "Estimate"], digits = 3)
ys <- max(Trends.offshore$Aragonite_Sat_W, na.rm =T)
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.offshore,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_Omega_Pred, aes(y = Of_Omega_Pred,
x=Date, colour = Sub_region), linetype=1, size=1)+
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_Omega_Modeled
Oceanic
hist(Trends.ocean$Aragonite_Sat_W)
shapiro.test(Trends.ocean$Aragonite_Sat_W)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$Aragonite_Sat_W
## W = 0.99088, p-value = 0.4623Omega_oc_0<-lmer(Aragonite_Sat_W ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
Omega_oc_1<-lmer(Aragonite_Sat_W ~ Year * Sub_region * Season++ (1|SiteID),
data=Trends.ocean)
#summary(Omega_oc_0)
#summary(Omega_oc_1)
#anova(Omega_oc_1)
anova(Omega_oc_0) ranova(Omega_oc_0) step(Omega_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 93.333 -168.665
## (1 | SiteID) 1 8 93.333 -170.665 0.00 1 1
## (1 | Month) 0 7 32.748 -51.496 121.17 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.003904 0.0019522 2 129.73 0.2008 0.81833
## Year 2 0.014173 0.0141725 1 136.94 1.4752 0.22661
## Sub_region 0 0.060793 0.0303965 2 132.85 3.1721 0.04511 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Sub_region + (1 | Month)Omega_oc<-lmer(Aragonite_Sat_W ~ Year + (1 | Month), data = Trends.ocean)
summary(Omega_oc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: Aragonite_Sat_W ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: -207.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.2014 -0.6785 -0.0475 0.6953 2.8765
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.030147 0.17363
## Residual 0.009914 0.09957
## Number of obs: 147, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 15.844366 9.970239 139.100423 1.589 0.114
## Year -0.005966 0.004941 139.093214 -1.207 0.229
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(Omega_oc) plot(Omega_oc)
qqnorm(resid(Omega_oc))
acf(resid(Omega_oc), main="acf(resid(Omega open))")
Arg_oc.emmc<-emmeans(Omega_oc, ~Year)
#Arg_oc.emmc<-multcomp::cld(Arg_oc.emmc)
#Arg_oc.emmc<-as.data.frame(Arg_in.emmc[complete.cases(Arg_in.emmc),])
#Arg_oc.emmc<-Arg_oc.emmc[order(Arg_oc.emmc$Sub_region),]
#Arg_oc.emmc# Create a new data frame for independent variables
Oc_Newdata<-expand.grid(one=unique(Trends.ocean$Zone),
Sub_region=unique(Trends.ocean$Sub_region),
Year=seq((min(Trends.ocean$Year)-0), (max(Trends.ocean$Year)+1)))
Oc_Omega_Pred <- predict(Omega_oc, newdata=Oc_Newdata, re.form=NA, level=0, asList = FALSE)
Oc_Omega_Pred <-cbind(Oc_Newdata, Oc_Omega_Pred)
Oc_Omega_Pred$Date<-as.Date(as.character(Oc_Omega_Pred$Year), format = "%Y")
# Model summary for labels
slope<-round(summary(Omega_oc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
#ys <- max(Trends.inshore$Aragonite_Sat_W, na.rm =T)
ys <- max(Trends.offshore$Aragonite_Sat_W, na.rm =T)
plot_label <- sprintf("\"\" ~ Omega[Ar] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_Omega_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(#limits = c(15,40),
#name=(expression("Aragonite saturation state" ~(Omega[Ar]))),
name=(expression(~Omega[Ar])),
limits = c(3, 4.5),
breaks = seq(0, 10, 0.5),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.ocean,
aes(y=Aragonite_Sat_W, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_Omega_Pred, aes(y = Oc_Omega_Pred,
x=Date), linetype=1, size=1, colour = "black") +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_Omega_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.Omega_in_0 <- data.frame (anova(Omega_in_0))
av.Omega_mc_0 <- data.frame (anova(Omega_mc_0) )
av.Omega_of_0 <- data.frame (anova(Omega_of_0))
av.Omega_oc_0 <- data.frame (anova(Omega_oc_0))
# Putting all into one dataframe/table
anova_Omega_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.Omega_in_0, av.Omega_mc_0,
av.Omega_of_0, av.Omega_oc_0)))
colnames(anova_Omega_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_Omega_0) <- NULL
# create HTML table using kableExtra
anova_Omega_0 %>% kable("html", digits=2,
caption="Initial omega models fixed factors: Year*Region") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| Year | 0.00 | 0.00 | 1 | 328.73 | 0.00 | 0.95 |
| Sub_region | 0.04 | 0.01 | 3 | 318.47 | 0.12 | 0.95 |
| Year:Sub_region | 0.04 | 0.01 | 3 | 318.47 | 0.12 | 0.95 |
| Omega mid-channel | ||||||
| Year | 0.01 | 0.01 | 1 | 304.60 | 0.29 | 0.59 |
| Sub_region | 0.06 | 0.02 | 3 | 293.59 | 0.37 | 0.78 |
| Year:Sub_region | 0.06 | 0.02 | 3 | 293.59 | 0.37 | 0.78 |
| Omega offshore | ||||||
| Year | 0.03 | 0.03 | 1 | 371.83 | 1.77 | 0.18 |
| Sub_region | 0.07 | 0.02 | 3 | 360.94 | 1.41 | 0.24 |
| Year:Sub_region | 0.07 | 0.02 | 3 | 360.94 | 1.40 | 0.24 |
| Omega ocean | ||||||
| Year | 0.01 | 0.01 | 1 | 134.71 | 1.40 | 0.24 |
| Sub_region | 0.00 | 0.00 | 2 | 129.71 | 0.20 | 0.82 |
| Year:Sub_region | 0.00 | 0.00 | 2 | 129.71 | 0.20 | 0.82 |
# R-ANOVAs
rav.Omega_in_0 <- data.frame (ranova(Omega_in_0))
rav.Omega_mc_0 <- data.frame (ranova(Omega_mc_0) )
rav.Omega_of_0 <- data.frame (ranova(Omega_of_0))
rav.Omega_oc_0 <- data.frame (ranova(Omega_oc_0))
# Putting all into one dataframe/table
ranova_Omega_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.Omega_in_0, rav.Omega_mc_0,
rav.Omega_of_0, rav.Omega_oc_0)))
colnames(ranova_Omega_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_Omega_0) <- NULL
# create HTML table using kableExtra
ranova_Omega_0 %>% kable("html", digits=2,
caption="Initial omega models random factors: Month+Station") %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| <none> | 11 | -146.35 | 314.69 | |||
| (1 | Month) | 10 | -264.88 | 549.77 | 237.08 | 1 | 0.00 |
| (1 | SiteID) | 10 | -147.81 | 315.62 | 2.93 | 1 | 0.09 |
| Omega mid-channel | ||||||
| <none> | 11 | -15.20 | 52.40 | |||
| (1 | Month) | 10 | -79.03 | 178.07 | 127.67 | 1 | 0.00 |
| (1 | SiteID) | 10 | -16.13 | 52.25 | 1.86 | 1 | 0.17 |
| Omega offshore | ||||||
| <none> | 11 | 208.48 | -394.97 | |||
| (1 | Month) | 10 | 112.36 | -204.72 | 192.25 | 1 | 0.00 |
| (1 | SiteID) | 10 | 207.77 | -395.53 | 1.44 | 1 | 0.23 |
| Omega ocean | ||||||
| <none> | 9 | 93.33 | -168.67 | |||
| (1 | Month) | 8 | 32.75 | -49.50 | 121.17 | 1 | 0.00 |
| (1 | SiteID) | 8 | 93.33 | -170.67 | 0.00 | 1 | 1.00 |
Omega_models_0<-list("Inshore" = Omega_in_0,
"Mid channel" = Omega_mc_0,
"Offshore" = Omega_of_0,
"Ocean" = Omega_oc_0)
modelsummary(Omega_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Initial Omega model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.007 | −0.001 | −0.008 | −0.007 |
| (0.020) | (0.014) | (0.008) | (0.010) | |
| Intercept | 18.461 | 5.878 | 20.923 | 18.800 |
| (40.951) | (28.305) | (15.637) | (19.179) | |
| Upper Keys | −19.565 | 4.078 | −12.566 | |
| (51.874) | (38.927) | (19.350) | ||
| Middle Keys | −29.358 | −18.516 | 8.870 | 1.129 |
| (50.315) | (34.434) | (19.165) | (21.769) | |
| Lower Keys | −15.957 | −27.793 | −22.561 | −9.356 |
| (50.674) | (34.770) | (18.267) | (21.903) | |
| Num.Obs. | 344 | 318 | 388 | 147 |
| R2 Marg. | 0.121 | 0.081 | 0.042 | 0.059 |
| R2 Cond. | 0.620 | 0.477 | 0.554 | |
| AIC | 314.7 | 52.4 | −395.0 | −168.7 |
| BIC | 356.9 | 93.8 | −351.4 | −141.8 |
| ICC | 0.6 | 0.4 | 0.5 | |
| RMSE | 0.32 | 0.22 | 0.12 | 0.09 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Models with non-significant factors removed
Omega_models<-list("Inshore" = Omega_in,
"Mid channel" = Omega_mc,
"Offshore" = Omega_of,
"Ocean" = Omega_oc)
modelsummary(Omega_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'Final Omega model outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.005 | −0.004 | −0.006 | |
| (0.008) | (0.004) | (0.005) | ||
| Intercept | 3.658*** | −7.360 | 12.654 | 15.844 |
| (0.115) | (15.532) | (7.903) | (9.970) | |
| Upper Keys | 0.201*** | 0.140*** | 0.030 | |
| (0.054) | (0.041) | (0.020) | ||
| Middle Keys | 0.092+ | 0.058 | 0.024 | |
| (0.053) | (0.036) | (0.020) | ||
| Lower Keys | −0.281*** | −0.100** | −0.051** | |
| (0.053) | (0.036) | (0.019) | ||
| Num.Obs. | 344 | 318 | 388 | 147 |
| R2 Marg. | 0.123 | 0.080 | 0.038 | 0.004 |
| R2 Cond. | 0.611 | 0.464 | 0.542 | 0.754 |
| AIC | 283.4 | 27.9 | −420.9 | −199.1 |
| BIC | 306.4 | 54.2 | −393.2 | −187.2 |
| ICC | 0.6 | 0.4 | 0.5 | 0.8 |
| RMSE | 0.32 | 0.22 | 0.12 | 0.10 |
| + p |
modelsummary(Omega_models, output = "FLK_results/Figures/Fig_2/Trend_Omega.docx")
#modelsummary(Omega_models, estimate = "p.value")Trends_Omega<-ggarrange(In_Omega_Modeled+ theme(
#axis.text.x=element_blank(),
legend.position = "none"),
Mc_Omega_Modeled+ theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Of_Omega_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
Oc_Omega_Modeled + theme(legend.position = "none",
#axis.text.x=element_blank(),
axis.title.y=element_blank()),
labels = c("C"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_Omega
pCO2-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$pCO2_uatm
## W = 0.94805, p-value < 2.2e-16
## Model with all data
pCO2_all_o<-lmer(pCO2_uatm~Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(Omega_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 -275.96 617.91
## (1 | SiteID) 1 32 -276.49 616.97 1.059 1 0.3035
## (1 | Month) 0 31 -432.83 927.66 312.693 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Zone:Sub_region 1 0.1748 0.02185 8 1156.1 0.2736 0.97455
## Year:Sub_region 2 0.0829 0.02764 3 1164.2 0.3478 0.79074
## Year:Zone 0 0.8539 0.28463 3 1167.1 3.5882 0.01334
## Zone:Sub_region 0 5.8363 0.72953 8 1167.1 9.1967 2.436e-12
##
## Year:Zone:Sub_region
## Year:Sub_region
## Year:Zone *
## Zone:Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## Aragonite_Sat_W ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone + Zone:Sub_regionpCO2_all_o_1<-lmer(pCO2_uatm ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
pCO2_all_o_3<-lmer(pCO2_uatm ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(pCO2_all_o, pCO2_all_o_1, pCO2_all_o_3) summary(pCO2_all_o_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: 12715.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5056 -0.5118 -0.0895 0.4862 5.7438
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1952 44.19
## Residual 2534 50.34
## Number of obs: 1197, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6761.657 2868.834 1175.441 2.357 0.018590
## Year -3.135 1.422 1175.416 -2.205 0.027618
## ZoneMid channel -6144.663 3782.512 1166.923 -1.624 0.104541
## ZoneOffshore -11332.404 3601.575 1166.912 -3.147 0.001694
## ZoneOceanic -15047.197 4756.872 1166.949 -3.163 0.001600
## Sub_regionUK -26.461 8.174 1166.988 -3.237 0.001241
## Sub_regionMK -28.997 8.072 1166.994 -3.592 0.000341
## Sub_regionLK 11.401 8.024 1167.024 1.421 0.155611
## Year:ZoneMid channel 3.036 1.875 1166.923 1.620 0.105562
## Year:ZoneOffshore 5.607 1.785 1166.912 3.142 0.001722
## Year:ZoneOceanic 7.442 2.357 1166.949 3.157 0.001637
## ZoneMid channel:Sub_regionUK 11.244 12.268 1167.005 0.917 0.359578
## ZoneOffshore:Sub_regionUK 19.579 11.512 1166.985 1.701 0.089268
## ZoneOceanic:Sub_regionUK 27.594 13.978 1167.012 1.974 0.048600
## ZoneMid channel:Sub_regionMK 18.743 11.411 1166.962 1.643 0.100743
## ZoneOffshore:Sub_regionMK 20.827 11.374 1166.963 1.831 0.067328
## ZoneOceanic:Sub_regionMK 36.058 11.726 1166.977 3.075 0.002153
## ZoneMid channel:Sub_regionLK -3.927 11.351 1166.959 -0.346 0.729418
## ZoneOffshore:Sub_regionLK -10.698 11.046 1166.963 -0.969 0.332981
##
## (Intercept) *
## Year *
## ZoneMid channel
## ZoneOffshore **
## ZoneOceanic **
## Sub_regionUK **
## Sub_regionMK ***
## Sub_regionLK
## Year:ZoneMid channel
## Year:ZoneOffshore **
## Year:ZoneOceanic **
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK .
## ZoneOceanic:Sub_regionUK *
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK .
## ZoneOceanic:Sub_regionMK **
## ZoneMid channel:Sub_regionLK
## ZoneOffshore:Sub_regionLK
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(pCO2_all_o_1) ranova(pCO2_all_o_1)# Obtain slopes
ranef(pCO2_all_o_1)## $Month
## (Intercept)
## Apr -56.973217
## May -54.622146
## Jun 4.627320
## Jul 44.022802
## Aug 33.795553
## Sep 61.345527
## Oct 51.108885
## Nov 21.816965
## Dec 3.170544
## Jan -9.926005
## Feb -51.860030
## Mar -46.506197
##
## with conditional variances for "Month" fixef(pCO2_all_o_1)## (Intercept) Year
## 6761.657108 -3.135358
## ZoneMid channel ZoneOffshore
## -6144.662813 -11332.404129
## ZoneOceanic Sub_regionUK
## -15047.196693 -26.461113
## Sub_regionMK Sub_regionLK
## -28.996527 11.401282
## Year:ZoneMid channel Year:ZoneOffshore
## 3.036177 5.607421
## Year:ZoneOceanic ZoneMid channel:Sub_regionUK
## 7.441633 11.243785
## ZoneOffshore:Sub_regionUK ZoneOceanic:Sub_regionUK
## 19.578823 27.594357
## ZoneMid channel:Sub_regionMK ZoneOffshore:Sub_regionMK
## 18.743369 20.827091
## ZoneOceanic:Sub_regionMK ZoneMid channel:Sub_regionLK
## 36.057819 -3.927243
## ZoneOffshore:Sub_regionLK
## -10.698358 coef(pCO2_all_o_1)$Month coef(pCO2_all_o_1)$Zone## NULL m.lst <- lstrends(pCO2_all_o_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore -3.1354 1.42 1175 -5.927 -0.344
## Mid channel -0.0992 1.47 1175 -2.974 2.776
## Offshore 2.4721 1.35 1176 -0.168 5.112
## Oceanic 4.3063 2.04 1171 0.303 8.310
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel -3.04 1.87 1167 -1.620 0.3678
## Inshore - Offshore -5.61 1.78 1167 -3.142 0.0093
## Inshore - Oceanic -7.44 2.36 1167 -3.157 0.0089
## Mid channel - Offshore -2.57 1.82 1167 -1.413 0.4913
## Mid channel - Oceanic -4.41 2.38 1167 -1.848 0.2512
## Offshore - Oceanic -1.83 2.31 1167 -0.793 0.8577
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimatesInshore
# Inshore pCO2
hist(Trends.inshore$pCO2_uatm)
shapiro.test(Trends.inshore$pCO2_uatm)# Normality NOT? rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$pCO2_uatm
## W = 0.98938, p-value = 0.01335 pCO2_in_0<-lmer(pCO2_uatm~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(pCO2_in_0)
anova(pCO2_in_0) ranova(pCO2_in_0) step(pCO2_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1915.9 3853.9
## (1 | Month) 0 10 -2051.9 4123.8 271.971 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1919.1 3858.2 6.346 1 0.01176 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 9819.4 3273.1 3 318.28 0.8174 0.4850
## Year 2 456.1 456.1 1 330.35 0.1141 0.7358
## Sub_region 3 30485.5 10161.8 3 7.03 2.5490 0.1386
##
## Model found:
## pCO2_uatm ~ (1 | Month) + (1 | SiteID)pCO2_in<-lmer(pCO2_uatm ~ (1|Month)+(1|SiteID), data=Trends.inshore)
summary(pCO2_in)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3880.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.6603 -0.6292 -0.0884 0.5648 3.8565
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6913.0 83.14
## SiteID (Intercept) 438.5 20.94
## Residual 3986.2 63.14
## Number of obs: 344, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 424.33 25.08 12.43 16.92 5.91e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 anova(pCO2_in) plot(pCO2_in)
qqnorm(resid(pCO2_in))
acf(resid(pCO2_in), main="acf(resid(pCO2 Inshore))") 
#pCO2_in.emmc<-emmeans(pCO2_in, ~Month)
#pCO2_in.emmc<-multcomp::cld(pCO2_in.emmc)
#pCO2_in.emmc<-pCO2_in.emmc[order(pCO2_in.emmc$Sub_region),]
#pCO2_in.emmc# Predict values
In_pCO2_Pred <- predict(pCO2_in, newdata=In_Newdata, re.form=NA, level=0, asList = FALSE)
In_pCO2_Pred <-cbind(In_Newdata, In_pCO2_Pred)
In_pCO2_Pred$Date<-as.Date(as.character(In_pCO2_Pred$Year), format = "%Y")
# # Model summary
# slope<-round(
# summary(pCO2_in)$coefficients["Year", "Estimate"], digits = 3)
# xs <- as.Date(as.character("2018-11-20"))
# ys <- max(Trends.midchanel$pCO2_uatm, na.rm =T)
#
# # Plot raw data point and fitted model
# plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
# plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
In_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(200,800),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=pCO2_uatm, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=21)+
geom_line(data=In_pCO2_Pred, aes(y = In_pCO2_Pred,
x=Date), linetype=2, size=1, colour = "black")
In_pCO2_Modeled
Mid channel
hist(Trends.midchanel$pCO2_uatm)
shapiro.test(Trends.midchanel$pCO2_uatm)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$pCO2_uatm
## W = 0.99193, p-value = 0.08115pCO2_mc_0<-lmer(pCO2_uatm~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(pCO2_mc_0)
anova(pCO2_mc_0) ranova(pCO2_mc_0) step(pCO2_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1596.5 3215.0
## (1 | SiteID) 1 10 -1597.2 3214.4 1.427 1 0.2323
## (1 | Month) 0 9 -1698.7 3415.5 203.028 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 5119.6 1706.5 3 299.05 1.2591 0.2885697
## Year 2 424.7 424.7 1 312.01 0.3126 0.5765201
## Sub_region 0 25387.8 8462.6 3 303.07 6.2450 0.0003984 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ Sub_region + (1 | Month)pCO2_mc<-lmer(pCO2_uatm ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(pCO2_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3209.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0816 -0.6257 -0.1400 0.6184 4.0531
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1790 42.31
## Residual 1359 36.86
## Number of obs: 318, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1847.0047 2557.7681 312.0201 0.722 0.4708
## Year -0.7086 1.2675 312.0143 -0.559 0.5765
## Sub_regionUK -15.5826 6.7090 302.0770 -2.323 0.0209 *
## Sub_regionMK -9.9211 5.9178 302.0364 -1.676 0.0947 .
## Sub_regionLK 7.6569 5.9080 302.1158 1.296 0.1960
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.019 0.018
## Sub_reginMK -0.004 0.003 0.528
## Sub_reginLK -0.026 0.024 0.531 0.602 anova(pCO2_mc) plot(pCO2_mc)
qqnorm(resid(pCO2_mc))
acf(resid(pCO2_mc), main="acf(resid(pCO2 Mid Channel))") 
pCO2_mc.emmc<-emmeans(pCO2_mc, ~Sub_region+Year)
pCO2_mc.emmc<-multcomp::cld(pCO2_mc.emmc)
pCO2_mc.emmc<-pCO2_mc.emmc[order(
pCO2_mc.emmc$Sub_region),]
pCO2_mc.emmc #write.csv(pCO2_mc.emmc, "FLK_results/Model_2_LTpCO2_POST.csv")# Predict values
Mc_pCO2_Pred <- predict(pCO2_mc, newdata=Mc_Newdata, re.form=NA, level=0, asList = FALSE)
Mc_pCO2_Pred <-cbind(Mc_Newdata, Mc_pCO2_Pred)
Mc_pCO2_Pred$Date<-as.Date(as.character(Mc_pCO2_Pred$Year), format = "%Y")
# Model summary
slope<-round(
summary(pCO2_mc)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pCO2_uatm, na.rm =T)
ys <- max(Trends.midchanel$pCO2_uatm, na.rm =T)+50
# Plot raw data point and fitted model
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Mc_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(200,800),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.midchanel,
aes(y=pCO2_uatm, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_pCO2_Pred, aes(y = Mc_pCO2_Pred,
x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_pCO2_Modeled
Offshore
hist(Trends.offshore$pCO2_uatm)
shapiro.test(Trends.offshore$pCO2_uatm)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$pCO2_uatm
## W = 0.98049, p-value = 4.25e-05pCO2_of_0<-lmer(pCO2_uatm~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(pCO2_of_0)
anova(pCO2_of_0) ranova(pCO2_of_0) step(pCO2_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1754.3 3530.6
## (1 | SiteID) 1 10 -1754.3 3528.6 0.00 1 1
## (1 | Month) 0 9 -1888.3 3794.7 268.04 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 0 4488 1496 3 368.87 3.1239 0.02594 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ Year + Sub_region + (1 | Month) + Year:Sub_regionpCO2_of<-lmer(pCO2_uatm ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(pCO2_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3525.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.8917 -0.6612 -0.0745 0.5456 5.1171
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 792.6 28.15
## Residual 487.1 22.07
## Number of obs: 388, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2868.7304 1388.0888 381.0773 -2.067 0.0394 *
## Year 1.6282 0.6879 381.0643 2.367 0.0184 *
## Sub_regionUK -5.4089 3.5549 371.8480 -1.522 0.1290
## Sub_regionMK -8.5154 3.5165 371.8470 -2.422 0.0159 *
## Sub_regionLK 2.0882 3.3455 371.9472 0.624 0.5329
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.008 0.006
## Sub_reginMK -0.007 0.006 0.590
## Sub_reginLK -0.021 0.019 0.622 0.630 anova(pCO2_of) plot(pCO2_of)
qqnorm(resid(pCO2_of))
acf(resid(Omega_in), main="acf(resid(pCO2 Offshore))") 
pCO2_of.emmc<-emmeans(pCO2_of, ~Sub_region)
pCO2_of.emmc<-multcomp::cld(pCO2_of.emmc)
pCO2_of.emmc<-pCO2_of.emmc[order(pCO2_of.emmc$Sub_region),]
pCO2_of.emmc #write.csv(pCO2_of.emmc, "FLK_results/Model_3_LTpCO2_POST.csv")# Predict values
Of_pCO2_Pred <- predict(pCO2_of, newdata=Of_Newdata, re.form=NA, level=0, asList = FALSE)
Of_pCO2_Pred <-cbind(Of_Newdata, Of_pCO2_Pred)
Of_pCO2_Pred$Date<-as.Date(as.character(Of_pCO2_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pCO2_of)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
ys <- max(Trends.offshore$pCO2_uatm, na.rm =T) -15
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(300,605),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.offshore,
aes(y=pCO2_uatm, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_pCO2_Pred, aes(y = Of_pCO2_Pred,
x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
#label = expression("~Omega[Ar] == -0.015** ~Year ^-1"),
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_pCO2_Modeled
Oceanic
hist(Trends.ocean$pCO2_uatm)
shapiro.test(Trends.ocean$pCO2_uatm)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$pCO2_uatm
## W = 0.97367, p-value = 0.006268pCO2_oc_0<-lmer(pCO2_uatm ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(pCO2_oc_0)
anova(pCO2_oc_0) ranova(pCO2_oc_0) step(pCO2_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -638.51 1295.0
## (1 | SiteID) 1 8 -638.51 1293.0 0.000 1 1
## (1 | Month) 0 7 -672.07 1358.2 67.127 1 2.546e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 305.77 152.88 2 130.07 0.4532 0.6366
## Sub_region 2 847.30 423.65 2 132.00 1.2679 0.2848
## Year 0 1510.86 1510.86 1 143.84 4.5007 0.0356 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pCO2_uatm ~ Year + (1 | Month)pCO2_oc<-lmer(pCO2_uatm ~ Year + (1 | Month),
data = Trends.ocean)
summary(pCO2_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pCO2_uatm ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1295.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.68545 -0.58477 -0.03898 0.58620 2.80401
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 388.9 19.72
## Residual 335.7 18.32
## Number of obs: 147, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -3417.6663 1805.0046 143.8374 -1.893 0.0603 .
## Year 1.8977 0.8945 143.8347 2.121 0.0356 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(pCO2_oc) plot(pCO2_oc)
qqnorm(resid(pCO2_oc))
acf(resid(pCO2_oc), main="acf(resid(pCO2 open))")
pCO2_oc.emmc<-emmeans(pCO2_oc, ~Year)
pCO2_oc.emmc<-multcomp::cld(pCO2_oc.emmc)
#pCO2_oc.emmc<-pCO2_oc.emmc[order(pCO2_oc.emmc$Sub_region),]
pCO2_oc.emmc# Predict values
Oc_pCO2_Pred <- predict(pCO2_oc, newdata=Oc_Newdata,
re.form=NA, level=0, asList = FALSE)
Oc_pCO2_Pred <-cbind(Oc_Newdata, Oc_pCO2_Pred)
Oc_pCO2_Pred$Date<-as.Date(as.character(Oc_pCO2_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pCO2_oc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
ys <- max(Trends.offshore$pCO2_uatm, na.rm =T)-15
plot_label <- sprintf("\"\" ~ pCO[2] == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_pCO2_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=expression(paste(pCO[2], ~" (", mu, " atm)")),
limits = c(300,605),
breaks = seq(0, 800, 100),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.ocean,
aes(y=pCO2_uatm, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_pCO2_Pred, aes(y = Oc_pCO2_Pred, x=Date),
colour = "black", linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_pCO2_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.pCO2_in_0 <- data.frame (anova(pCO2_in_0))
av.pCO2_mc_0 <- data.frame (anova(pCO2_mc_0) )
av.pCO2_of_0 <- data.frame (anova(pCO2_of_0))
av.pCO2_oc_0 <- data.frame (anova(pCO2_oc_0))
# Putting all into one dataframe/table
anova_pH <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.pCO2_in_0, av.pCO2_mc_0,
av.pCO2_of_0, av.pCO2_oc_0)))
colnames(anova_pH) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_pH) <- NULL
# create HTML table using kableExtra
anova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pCO2 inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pCO2 mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pCO2 offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pCO2 ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| pCO2 inshore | ||||||
| Year | 403.31 | 403.31 | 1 | 327.14 | 0.10 | 0.75 |
| Sub_region | 9863.53 | 3287.84 | 3 | 318.29 | 0.82 | 0.48 |
| Year:Sub_region | 9819.44 | 3273.15 | 3 | 318.28 | 0.82 | 0.49 |
| pCO2 mid-channel | ||||||
| Year | 289.60 | 289.60 | 1 | 303.67 | 0.22 | 0.64 |
| Sub_region | 5209.62 | 1736.54 | 3 | 293.74 | 1.30 | 0.27 |
| Year:Sub_region | 5190.90 | 1730.30 | 3 | 293.74 | 1.30 | 0.28 |
| pCO2 offshore | ||||||
| Year | 3634.86 | 3634.86 | 1 | 377.77 | 7.59 | 0.01 |
| Sub_region | 4496.27 | 1498.76 | 3 | 368.89 | 3.13 | 0.03 |
| Year:Sub_region | 4488.04 | 1496.01 | 3 | 368.89 | 3.12 | 0.03 |
| pCO2 ocean | ||||||
| Year | 1123.31 | 1123.31 | 1 | 139.52 | 3.33 | 0.07 |
| Sub_region | 305.88 | 152.94 | 2 | 130.10 | 0.45 | 0.64 |
| Year:Sub_region | 305.77 | 152.88 | 2 | 130.10 | 0.45 | 0.64 |
# R-ANOVAs
rav.pCO2_in_0 <- data.frame (ranova(pCO2_in_0))
rav.pCO2_mc_0 <- data.frame (ranova(pCO2_mc_0) )
rav.pCO2_of_0 <- data.frame (ranova(pCO2_of_0))
rav.pCO2_oc_0 <- data.frame (ranova(pCO2_oc_0))
# Putting all into one dataframe/table
ranova_pCO2 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.pCO2_in_0, rav.pCO2_mc_0,
rav.pCO2_of_0, rav.pCO2_oc_0)))
colnames(ranova_pCO2) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_pCO2) <- NULL
# create HTML table using kableExtra
ranova_pCO2 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "Omega inshore", 1, 3) %>% # groups rows with label
pack_rows(., "Omega mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "Omega offshore", 7, 9) %>% # groups rows with label
pack_rows(., "Omega ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| Omega inshore | ||||||
| <none> | 11 | -1915.93 | 3853.87 | |||
| (1 | Month) | 10 | -2051.92 | 4123.84 | 271.97 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1919.11 | 3858.21 | 6.35 | 1 | 0.01 |
| Omega mid-channel | ||||||
| <none> | 11 | -1596.51 | 3215.02 | |||
| (1 | Month) | 10 | -1698.74 | 3417.47 | 204.45 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1597.22 | 3214.45 | 1.43 | 1 | 0.23 |
| Omega offshore | ||||||
| <none> | 11 | -1754.32 | 3530.63 | |||
| (1 | Month) | 10 | -1888.34 | 3796.67 | 268.04 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1754.32 | 3528.63 | 0.00 | 1 | 1.00 |
| Omega ocean | ||||||
| <none> | 9 | -638.51 | 1295.02 | |||
| (1 | Month) | 8 | -672.07 | 1360.15 | 67.13 | 1 | 0.00 |
| (1 | SiteID) | 8 | -638.51 | 1293.02 | 0.00 | 1 | 1.00 |
pCO2_models_0<-list("Inshore" = pCO2_in_0,
"Mid channel" = pCO2_mc_0,
"Offshore" = pCO2_of_0,
"Ocean" = pCO2_oc_0)
modelsummary(pCO2_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pCO model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 1.030 | 0.459 | 3.299* | 1.154 |
| (3.905) | (2.287) | (1.359) | (1.760) | |
| Intercept | −1641.939 | −509.420 | −6239.564* | −1922.057 |
| (7879.653) | (4615.835) | (2741.383) | (3552.088) | |
| Upper Keys | 4095.178 | 959.391 | 3881.748 | |
| (9973.579) | (6330.034) | (3388.676) | ||
| Middle Keys | −1726.157 | −810.299 | −181.767 | −3044.843 |
| (9673.247) | (5599.500) | (3356.720) | (4051.995) | |
| Lower Keys | 11347.243 | 8340.861 | 7481.548* | −354.996 |
| (9742.322) | (5654.208) | (3199.251) | (4079.280) | |
| Num.Obs. | 344 | 318 | 388 | 147 |
| R2 Marg. | 0.026 | 0.030 | 0.032 | 0.066 |
| R2 Cond. | 0.649 | 0.591 | 0.635 | |
| AIC | 3853.9 | 3215.0 | 3530.6 | 1295.0 |
| BIC | 3896.1 | 3256.4 | 3574.2 | 1321.9 |
| ICC | 0.6 | 0.6 | 0.6 | |
| RMSE | 61.09 | 35.26 | 21.35 | 17.33 |
| + p |
#modelsummary(Omega_models, estimate = "p.value")Factors removed models
pCO2_models<-list("Inshore" = pCO2_in,
"Mid channel" = pCO2_mc,
"Offshore" = pCO2_of,
"Ocean" = pCO2_oc)
modelsummary(pCO2_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pCO2 models outputs (dropped factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.709 | 1.628* | 1.898* | |
| (1.268) | (0.688) | (0.895) | ||
| Intercept | 424.332*** | 1847.005 | −2868.730* | −3417.666+ |
| (25.078) | (2557.768) | (1388.089) | (1805.005) | |
| Upper Keys | −15.583* | −5.409 | ||
| (6.709) | (3.555) | |||
| Middle Keys | −9.921+ | −8.515* | ||
| (5.918) | (3.516) | |||
| Lower Keys | 7.657 | 2.088 | ||
| (5.908) | (3.346) | |||
| Num.Obs. | 344 | 318 | 388 | 147 |
| R2 Marg. | 0.000 | 0.025 | 0.024 | 0.022 |
| R2 Cond. | 0.648 | 0.579 | 0.628 | 0.547 |
| AIC | 3888.4 | 3223.3 | 3539.3 | 1303.2 |
| BIC | 3903.8 | 3249.6 | 3567.1 | 1315.1 |
| ICC | 0.6 | 0.6 | 0.6 | 0.5 |
| RMSE | 61.32 | 35.95 | 21.62 | 17.55 |
| + p |
#modelsummary(pCO2_models, estimate = "p.value")Trends_pCO2<-ggarrange(In_pCO2_Modeled+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Mc_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
Of_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
Oc_pCO2_Modeled + theme(legend.position = "none",
axis.title.y=element_blank(),
axis.text.x=element_blank()),
labels = c("A"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_pCO2
pH-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$pH_calculated
## W = 0.9654, p-value = 2.775e-16
## Model with all data
pH_all_o<-lmer(pH_calculated ~ Year * Zone * Sub_region + (1|Month)+
(1|SiteID), data=Trends.data)
step(pH_all_o)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 33 1936.9 -3807.8
## (1 | Month) 0 32 1606.8 -3149.7 660.10 1 < 2e-16 ***
## (1 | SiteID) 0 32 1935.4 -3806.8 2.94 1 0.08666 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value
## Year:Zone:Sub_region 1 0.001667 0.0002083 8 1134.60 0.1180
## Year:Sub_region 2 0.009967 0.0033223 3 1142.16 1.8934
## Year:Zone 0 0.031830 0.0106100 3 1145.11 6.0315
## Zone:Sub_region 0 0.038306 0.0047882 8 23.85 2.7220
## Pr(>F)
## Year:Zone:Sub_region 0.9985669
## Year:Sub_region 0.1288993
## Year:Zone 0.0004481 ***
## Zone:Sub_region 0.0275848 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Year + Zone + Sub_region + (1 | Month) + (1 | SiteID) + Year:Zone + Zone:Sub_regionpH_all_o_1<-lmer(pH_calculated ~ Year + Zone + Sub_region + (1 | Month) +
Year:Zone + Zone:Sub_region, data=Trends.data)
pH_all_o_3<-lmer(pH_calculated ~ Year + Zone + (1 | Month) + (1|Sub_region)+
Year:Zone, data=Trends.data)
anova(pH_all_o, pH_all_o_1, pH_all_o_3) summary(pH_all_o_1)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Zone + Sub_region + (1 | Month) + Year:Zone +
## Zone:Sub_region
## Data: Trends.data
##
## REML criterion at convergence: -3972.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.0535 -0.5326 0.0664 0.4908 4.7287
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.001793 0.04234
## Residual 0.001779 0.04218
## Number of obs: 1197, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.995e+00 2.405e+00 1.174e+03 0.830 0.406953
## Year 2.990e-03 1.192e-03 1.174e+03 2.509 0.012251
## ZoneMid channel 5.778e+00 3.170e+00 1.167e+03 1.823 0.068581
## ZoneOffshore 1.070e+01 3.018e+00 1.167e+03 3.547 0.000406
## ZoneOceanic 1.395e+01 3.986e+00 1.167e+03 3.499 0.000484
## Sub_regionUK 2.414e-02 6.850e-03 1.167e+03 3.524 0.000441
## Sub_regionMK 1.623e-02 6.764e-03 1.167e+03 2.399 0.016589
## Sub_regionLK -2.512e-02 6.724e-03 1.167e+03 -3.736 0.000196
## Year:ZoneMid channel -2.859e-03 1.571e-03 1.167e+03 -1.820 0.068980
## Year:ZoneOffshore -5.301e-03 1.496e-03 1.167e+03 -3.544 0.000409
## Year:ZoneOceanic -6.896e-03 1.976e-03 1.167e+03 -3.491 0.000499
## ZoneMid channel:Sub_regionUK -1.001e-02 1.028e-02 1.167e+03 -0.973 0.330638
## ZoneOffshore:Sub_regionUK -1.926e-02 9.647e-03 1.167e+03 -1.996 0.046169
## ZoneOceanic:Sub_regionUK -4.000e-02 1.171e-02 1.167e+03 -3.415 0.000659
## ZoneMid channel:Sub_regionMK -9.845e-03 9.562e-03 1.167e+03 -1.030 0.303418
## ZoneOffshore:Sub_regionMK -1.113e-02 9.531e-03 1.167e+03 -1.168 0.243018
## ZoneOceanic:Sub_regionMK -3.709e-02 9.826e-03 1.167e+03 -3.774 0.000168
## ZoneMid channel:Sub_regionLK 1.383e-02 9.512e-03 1.167e+03 1.454 0.146099
## ZoneOffshore:Sub_regionLK 2.311e-02 9.256e-03 1.167e+03 2.497 0.012667
##
## (Intercept)
## Year *
## ZoneMid channel .
## ZoneOffshore ***
## ZoneOceanic ***
## Sub_regionUK ***
## Sub_regionMK *
## Sub_regionLK ***
## Year:ZoneMid channel .
## Year:ZoneOffshore ***
## Year:ZoneOceanic ***
## ZoneMid channel:Sub_regionUK
## ZoneOffshore:Sub_regionUK *
## ZoneOceanic:Sub_regionUK ***
## ZoneMid channel:Sub_regionMK
## ZoneOffshore:Sub_regionMK
## ZoneOceanic:Sub_regionMK ***
## ZoneMid channel:Sub_regionLK
## ZoneOffshore:Sub_regionLK *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## fit warnings:
## fixed-effect model matrix is rank deficient so dropping 1 column / coefficient anova(pH_all_o_1) ranova(pH_all_o_1) ranef(pH_all_o_1)## $Month
## (Intercept)
## Apr 0.051180906
## May 0.045802555
## Jun -0.013693787
## Jul -0.048114395
## Aug -0.037501103
## Sep -0.058058743
## Oct -0.040662622
## Nov -0.017325016
## Dec 0.001336747
## Jan 0.014498595
## Feb 0.057032096
## Mar 0.045504768
##
## with conditional variances for "Month" fixef(pH_all_o_1)## (Intercept) Year
## 1.995441784 0.002990402
## ZoneMid channel ZoneOffshore
## 5.777879110 10.703787726
## ZoneOceanic Sub_regionUK
## 13.948737264 0.024141616
## Sub_regionMK Sub_regionLK
## 0.016227844 -0.025118073
## Year:ZoneMid channel Year:ZoneOffshore
## -0.002859224 -0.005300869
## Year:ZoneOceanic ZoneMid channel:Sub_regionUK
## -0.006896163 -0.010005057
## ZoneOffshore:Sub_regionUK ZoneOceanic:Sub_regionUK
## -0.019255358 -0.040004474
## ZoneMid channel:Sub_regionMK ZoneOffshore:Sub_regionMK
## -0.009845181 -0.011132693
## ZoneOceanic:Sub_regionMK ZoneMid channel:Sub_regionLK
## -0.037087964 0.013834338
## ZoneOffshore:Sub_regionLK
## 0.023111739 coef(pH_all_o_1)$Month coef(pH_all_o_1)$Zone## NULL m.lst <- lstrends(pH_all_o_1, "Zone", var="Year")
m.lst## Zone Year.trend SE df lower.CL upper.CL
## Inshore 0.002990 0.00119 1174 0.00065 5.33e-03
## Mid channel 0.000131 0.00123 1174 -0.00228 2.54e-03
## Offshore -0.002310 0.00113 1174 -0.00452 -9.71e-05
## Oceanic -0.003906 0.00171 1170 -0.00726 -5.51e-04
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95 pairs(m.lst)## contrast estimate SE df t.ratio p.value
## Inshore - Mid channel 0.00286 0.00157 1167 1.820 0.2642
## Inshore - Offshore 0.00530 0.00150 1167 3.544 0.0023
## Inshore - Oceanic 0.00690 0.00198 1167 3.491 0.0028
## Mid channel - Offshore 0.00244 0.00152 1167 1.601 0.3782
## Mid channel - Oceanic 0.00404 0.00200 1167 2.021 0.1806
## Offshore - Oceanic 0.00160 0.00194 1167 0.823 0.8436
##
## Results are averaged over the levels of: Sub_region
## Degrees-of-freedom method: kenward-roger
## P value adjustment: tukey method for comparing a family of 4 estimates # Create a new data frame for independent variables
Newdata<-expand.grid(Zone=unique(Trends.data$Zone),
Sub_region=unique(Trends.data$Sub_region),
Year=seq((min(Trends.data$Year)-0), (max(Trends.data$Year)+1)))
# Predict values
pH_Pred <- predict(pH_all_o_1, newdata=Newdata,
re.form=NA, level=0, asList = FALSE)
pH_Pred <-cbind(Newdata, pH_Pred)
pH_Pred$Date<-as.Date(as.character(pH_Pred$Year), format = "%Y")
pH_Modeled<-Long_plot2+ facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.data,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5,shape=21)+
geom_line(data=pH_Pred, aes(y = pH_Pred, x=Date, colour = Sub_region),
linetype=2, size=1) #+
#annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
# label =paste0(as.character(plot_label, plot_label2, sep = " ")),
# size=4)
pH_Modeled
Inshore
# Inshore pH
hist(Trends.inshore$pH_calculated)
shapiro.test(Trends.inshore$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$pH_calculated
## W = 0.99278, p-value = 0.09599pH_in_0<-lmer(pH_calculated~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(pH_in_0)
anova(pH_in_0) ranova(pH_in_0) step(pH_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 472.07 -922.13
## (1 | Month) 0 10 319.28 -618.56 305.577 1 < 2e-16 ***
## (1 | SiteID) 0 10 469.57 -919.14 4.998 1 0.02537 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.005513 0.0018377 3 318.36 0.6869 0.56063
## Year 2 0.000129 0.0001292 1 329.30 0.0484 0.82595
## Sub_region 0 0.043823 0.0146075 3 7.05 5.4908 0.02924 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID)pH_in<-lmer(pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID),
data=Trends.inshore)
summary(pH_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: -981.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.7731 -0.6334 -0.0156 0.5966 3.7945
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0056657 0.07527
## SiteID (Intercept) 0.0001457 0.01207
## Residual 0.0026604 0.05158
## Number of obs: 344, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 8.02929 0.02421 14.95201 331.714 <2e-16 ***
## Sub_regionUK 0.02205 0.01384 6.99886 1.593 0.155
## Sub_regionMK 0.01624 0.01378 6.87776 1.178 0.278
## Sub_regionLK -0.02408 0.01376 6.82725 -1.750 0.125
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.336
## Sub_reginMK -0.338 0.590
## Sub_reginLK -0.339 0.590 0.595 anova(pH_in) plot(pH_in)
qqnorm(resid(pH_in))
acf(resid(pH_in), main="acf(resid(pH Inshore))") 
pH_in.emmc<-emmeans(pH_in, ~Sub_region)
pH_in.emmc<-multcomp::cld(pH_in.emmc)
pH_in.emmc<-pH_in.emmc[order(pH_in.emmc$Sub_region),]
pH_in.emmc#write.csv(pH_in.emmc, "FLK_results/Model_5_LTpH_POST.csv")# Predict values
In_pH_Pred <- predict(pH_in, newdata=In_Newdata,
re.form=NA, level=0, asList = FALSE)
In_pH_Pred <-cbind(In_Newdata, In_pH_Pred)
In_pH_Pred$Date<-as.Date(as.character(In_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
# slope<-round(
# summary(pH_in)$coefficients["Year", "Estimate"], digits = 3)
# xs <- as.Date(as.character("2018-11-20"))
# ys <- max(Trends.inshore$pH_calculated, na.rm =T)
# plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
# plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
In_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.inshore,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5,shape=21)+
geom_line(data=In_pH_Pred, aes(y = In_pH_Pred, x=Date, colour = Sub_region),
linetype=2, size=1) #+
#annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
# label =paste0(as.character(plot_label, plot_label2, sep = " ")),
# size=4)
In_pH_Modeled
Mid channel
hist(Trends.midchanel$pH_calculated)
shapiro.test(Trends.midchanel$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$pH_calculated
## W = 0.98077, p-value = 0.0002898pH_mc_0<-lmer(pH_calculated~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(pH_mc_0)
anova(pH_mc_0) ranova(pH_mc_0) step(pH_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 578.68 -1135.4
## (1 | SiteID) 1 10 578.13 -1136.3 1.10 1 0.2942
## (1 | Month) 0 9 464.95 -911.9 226.37 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.0031628 0.0010543 3 298.99 0.9743 0.4051
## Year 2 0.0002549 0.0002549 1 311.15 0.2356 0.6277
## Sub_region 0 0.0279055 0.0093018 3 303.03 8.6244 1.645e-05
##
## Year:Sub_region
## Year
## Sub_region ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Sub_region + (1 | Month)pH_mc<-lmer(pH_calculated ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(pH_mc)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: -1184.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4722 -0.6346 0.0314 0.5378 4.1787
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.001708 0.04133
## Residual 0.001082 0.03289
## Number of obs: 318, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 6.928e+00 2.287e+00 3.112e+02 3.029 0.00266 **
## Year 5.501e-04 1.133e-03 3.112e+02 0.485 0.62772
## Sub_regionUK 1.450e-02 5.986e-03 3.020e+02 2.422 0.01602 *
## Sub_regionMK 6.195e-03 5.280e-03 3.020e+02 1.173 0.24162
## Sub_regionLK -1.128e-02 5.272e-03 3.021e+02 -2.140 0.03312 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.019 0.018
## Sub_reginMK -0.004 0.003 0.528
## Sub_reginLK -0.026 0.025 0.531 0.602 anova(pH_mc) plot(pH_mc)
qqnorm(resid(pH_mc))
acf(resid(pH_mc), main="acf(resid(pH Mid Channel))") 
pH_mc.emmc<-emmeans(pH_mc, ~Sub_region)
pH_mc.emmc<-multcomp::cld(pH_mc.emmc)
pH_mc.emmc<-pH_mc.emmc[order(pH_mc.emmc$Sub_region),]
pH_mc.emmc #write.csv(pH_mc.emmc, "FLK_results/Model_6_LTpH_POST.csv")# Predict values
Mc_pH_Pred <- predict(pH_mc, newdata=Mc_Newdata,
re.form=NA, level=0, asList = FALSE)
Mc_pH_Pred <-cbind(Mc_Newdata, Mc_pH_Pred)
Mc_pH_Pred$Date<-as.Date(as.character(Mc_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_mc)$coefficients["Year", "Estimate"], digits = 3)
#xs <- as.Date(as.character("2018-11-20"))
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.midchanel$pH_calculated, na.rm =T)+0.03
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Mc_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
breaks = seq(0, 10, 0.1),
limits=c(7.80, 8.28),
expand = c(0.02, 0.02)) +
geom_point(data=Trends.midchanel,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=23)+
geom_line(data=Mc_pH_Pred, aes(y = Mc_pH_Pred, x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Mc_pH_Modeled
Offshore
hist(Trends.offshore$pH_calculated)
shapiro.test(Trends.offshore$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$pH_calculated
## W = 0.99092, p-value = 0.01729pH_of_0<-lmer(pH_calculated~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(pH_of_0)
anova(pH_of_0) ranova(pH_of_0) step(pH_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 920.71 -1819.4
## (1 | SiteID) 1 10 920.71 -1821.4 0.00 1 1
## (1 | Month) 0 9 767.35 -1516.7 306.73 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 0 0.0033325 0.0011108 3 368.84 3.037 0.02913 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## pH_calculated ~ Year + Sub_region + (1 | Month) + Year:Sub_regionpH_of<-lmer(pH_calculated ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(pH_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: -1867.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.5593 -0.5678 0.0880 0.6617 3.0799
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0007452 0.02730
## Residual 0.0003717 0.01928
## Number of obs: 388, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.043e+01 1.215e+00 3.800e+02 8.591 2.26e-16 ***
## Year -1.188e-03 6.019e-04 3.799e+02 -1.973 0.0492 *
## Sub_regionUK 3.608e-03 3.106e-03 3.719e+02 1.162 0.2461
## Sub_regionMK 5.338e-03 3.072e-03 3.719e+02 1.738 0.0831 .
## Sub_regionLK -3.117e-03 2.923e-03 3.719e+02 -1.066 0.2869
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.008 0.006
## Sub_reginMK -0.007 0.006 0.590
## Sub_reginLK -0.021 0.019 0.622 0.630 anova(pH_of) plot(pH_of)
qqnorm(resid(pH_of))
acf(resid(pH_of), main="acf(resid(pH Offshore))")
pH_of.emmc<-emmeans(pH_of, ~Sub_region)
pH_of.emmc<-multcomp::cld(pH_of.emmc)
pH_of.emmc<-pH_of.emmc[order(pH_of.emmc$Sub_region),]
pH_of.emmc #write.csv(pH_of.emmc, "FLK_results/Model_7_LTpH_POST.csv")# Predict values
Of_pH_Pred <- predict(pH_of, newdata=Of_Newdata,
re.form=NA, level=0, asList = FALSE)
Of_pH_Pred <-cbind(Of_Newdata, Of_pH_Pred)
Of_pH_Pred$Date<-as.Date(as.character(Of_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_of)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.offshore$pH_calculated, na.rm =T)+0.08
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Of_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.88, 8.2),
# limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0, 0.01)) +
geom_point(data=Trends.offshore,
aes(y=pH_calculated, x=Date,
#fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=24)+
geom_line(data=Of_pH_Pred, aes(y = Of_pH_Pred, x=Date, colour = Sub_region),
linetype=1, size=1) +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Of_pH_Modeled
Oceanic
hist(Trends.ocean$pH_calculated)
shapiro.test(Trends.ocean$pH_calculated)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$pH_calculated
## W = 0.98038, p-value = 0.03364pH_oc_0<-lmer(pH_calculated ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(pH_oc_0)
anova(pH_oc_0) ranova(pH_oc_0) step(pH_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 352.05 -686.11
## (1 | SiteID) 1 8 352.05 -688.11 0.000 1 1
## (1 | Month) 0 7 315.65 -617.29 72.815 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 0.00017053 0.00008527 2 130.03 0.3221 0.7252
## Sub_region 2 0.00065586 0.00032793 2 131.94 1.2531 0.2890
## Year 3 0.00059507 0.00059507 1 143.36 2.2640 0.1346
##
## Model found:
## pH_calculated ~ (1 | Month)pH_oc<-lmer(pH_calculated ~ Year + (1 | Month),
data=Trends.ocean)
summary(pH_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: pH_calculated ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: -742.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.62360 -0.58071 -0.03828 0.65055 2.63861
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 0.0003381 0.01839
## Residual 0.0002628 0.01621
## Number of obs: 147, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1.045e+01 1.601e+00 1.434e+02 6.527 1.08e-09 ***
## Year -1.194e-03 7.935e-04 1.434e+02 -1.505 0.135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 anova(pH_oc) plot(pH_oc)
qqnorm(resid(pH_oc))
acf(resid(pH_oc), main="acf(resid(pH ocean))")
pH_oc.emmc<-emmeans(pH_oc, ~Year)
pH_oc.emmc<-multcomp::cld(pH_oc.emmc)
#pH_oc.emmc<-pH_oc.emmc[order(pH_oc.emmc$Sub_region),]
pH_oc.emmc# Predict values
Oc_pH_Pred <- predict(pH_oc, newdata=Oc_Newdata,
re.form=NA, level=0, asList = FALSE)
Oc_pH_Pred <-cbind(Oc_Newdata, Oc_pH_Pred)
Oc_pH_Pred$Date<-as.Date(as.character(Oc_pH_Pred$Year), format = "%Y")
# Model summary for slope labels
slope<-round(
summary(pH_oc)$coefficients["Year", "Estimate"], digits = 3)
#ys <- max(Trends.inshore$pH_calculated, na.rm =T)
ys <- max(Trends.offshore$pH_calculated, na.rm =T)+0.08
plot_label <- sprintf("\"\" ~ pH == %0.3f", slope)
plot_label2 <- expression("Year^-1")
# Plot raw data point and fitted model
Oc_pH_Modeled<-Long_plot2+ #facet_grid(~Zone)+
scale_y_continuous(name=(expression(pH[calculated])),
limits=c(7.88, 8.2),
# limits=c(7.80, 8.28),
breaks = seq(0, 10, 0.1),
expand = c(0, 0.01)) +
geom_point(data=Trends.ocean,
aes(y=pH_calculated, x=Date, #fill = Sub_region),
colour = Sub_region),
alpha=0.5, size=0.5, shape=25)+
geom_line(data=Oc_pH_Pred, aes(y = Oc_pH_Pred, x=Date),
linetype=1, size=1, colour = "black") +
annotate(geom="text", x = as.Date(xs), y = ys, parse=TRUE,
label =paste0(as.character(plot_label, plot_label2, sep = " ")),
size=4)
Oc_pH_Modeled
Models summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.pH_in_0 <- data.frame (anova(pH_in_0))
av.pH_mc_0 <- data.frame (anova(pH_mc_0) )
av.pH_of_0 <- data.frame (anova(pH_of_0))
av.pH_oc_0 <- data.frame (anova(pH_oc_0))
# Putting all into one dataframe/table
anova_pH <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.pH_in_0, av.pH_mc_0,
av.pH_of_0, av.pH_oc_0)))
colnames(anova_pH) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_pH) <- NULL
# create HTML table using kableExtra
anova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pH inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pH mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pH offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pH ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| pH inshore | ||||||
| Year | 0.00 | 0 | 1 | 326.18 | 0.01 | 0.91 |
| Sub_region | 0.01 | 0 | 3 | 318.36 | 0.69 | 0.56 |
| Year:Sub_region | 0.01 | 0 | 3 | 318.36 | 0.69 | 0.56 |
| pH mid-channel | ||||||
| Year | 0.00 | 0 | 1 | 302.76 | 0.13 | 0.72 |
| Sub_region | 0.00 | 0 | 3 | 293.60 | 1.00 | 0.39 |
| Year:Sub_region | 0.00 | 0 | 3 | 293.60 | 1.00 | 0.39 |
| pH offshore | ||||||
| Year | 0.00 | 0 | 1 | 376.66 | 5.67 | 0.02 |
| Sub_region | 0.00 | 0 | 3 | 368.90 | 3.04 | 0.03 |
| Year:Sub_region | 0.00 | 0 | 3 | 368.90 | 3.04 | 0.03 |
| pH ocean | ||||||
| Year | 0.00 | 0 | 1 | 139.04 | 1.51 | 0.22 |
| Sub_region | 0.00 | 0 | 2 | 130.03 | 0.32 | 0.73 |
| Year:Sub_region | 0.00 | 0 | 2 | 130.03 | 0.32 | 0.73 |
# R-ANOVAs
rav.pH_in_0 <- data.frame (ranova(pH_in_0))
rav.pH_mc_0 <- data.frame (ranova(pH_mc_0) )
rav.pH_of_0 <- data.frame (ranova(pH_of_0))
rav.pH_oc_0 <- data.frame (ranova(pH_oc_0))
# Putting all into one dataframe/table
ranova_pH <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.pH_in_0, rav.pH_mc_0,
rav.pH_of_0, rav.pH_oc_0)))
colnames(ranova_pH) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_pH) <- NULL
# create HTML table using kableExtra
ranova_pH %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "pH inshore", 1, 3) %>% # groups rows with label
pack_rows(., "pH mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "pH offshore", 7, 9) %>% # groups rows with label
pack_rows(., "pH ocean", 10, 12) | npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| pH inshore | ||||||
| <none> | 11 | 472.07 | -922.13 | |||
| (1 | Month) | 10 | 319.28 | -618.56 | 305.58 | 1 | 0.00 |
| (1 | SiteID) | 10 | 469.57 | -919.14 | 5.00 | 1 | 0.03 |
| pH mid-channel | ||||||
| <none> | 11 | 578.68 | -1135.37 | |||
| (1 | Month) | 10 | 464.95 | -909.90 | 227.47 | 1 | 0.00 |
| (1 | SiteID) | 10 | 578.13 | -1136.27 | 1.10 | 1 | 0.29 |
| pH offshore | ||||||
| <none> | 11 | 920.71 | -1819.43 | |||
| (1 | Month) | 10 | 767.35 | -1514.69 | 306.73 | 1 | 0.00 |
| (1 | SiteID) | 10 | 920.71 | -1821.43 | 0.00 | 1 | 1.00 |
| pH ocean | ||||||
| <none> | 9 | 352.05 | -686.11 | |||
| (1 | Month) | 8 | 315.65 | -615.29 | 72.81 | 1 | 0.00 |
| (1 | SiteID) | 8 | 352.05 | -688.11 | 0.00 | 1 | 1.00 |
pH_models_0<-list("Inshore" = pH_in_0,
"Mid channel" = pH_mc_0,
"Offshore" = pH_of_0,
"Ocean" = pH_oc_0)
modelsummary(pH_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pH model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.002 | −0.001 | −0.003* | 0.000 |
| (0.003) | (0.002) | (0.001) | (0.002) | |
| Intercept | 12.801* | 9.924* | 13.706*** | 8.959** |
| (6.444) | (4.132) | (2.397) | (3.149) | |
| Upper Keys | −3.966 | −1.882 | −3.793 | |
| (8.152) | (5.663) | (2.962) | ||
| Middle Keys | −5.637 | −1.331 | −0.425 | 2.596 |
| (7.907) | (5.009) | (2.934) | (3.590) | |
| Lower Keys | −11.099 | −7.659 | −6.779* | 0.871 |
| (7.963) | (5.058) | (2.796) | (3.613) | |
| Num.Obs. | 344 | 318 | 388 | 147 |
| R2 Marg. | 0.041 | 0.034 | 0.024 | 0.044 |
| R2 Cond. | 0.697 | 0.632 | 0.678 | |
| AIC | −922.1 | −1135.4 | −1819.4 | −686.1 |
| BIC | −879.9 | −1094.0 | −1775.9 | −659.2 |
| ICC | 0.7 | 0.6 | 0.7 | |
| RMSE | 0.05 | 0.03 | 0.02 | 0.02 |
| + p |
#modelsummary(pH_models, estimate = "p.value")Factors removed models
pH_models<-list("Inshore" = pH_in,
"Mid channel" = pH_mc,
"Offshore" = pH_of,
"Ocean" = pH_oc)
modelsummary(pH_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'pH model outputs (factors dropped)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.001 | −0.001* | −0.001 | |
| (0.001) | (0.001) | (0.001) | ||
| Intercept | 8.029*** | 6.928** | 10.434*** | 10.450*** |
| (0.024) | (2.287) | (1.215) | (1.601) | |
| Upper Keys | 0.022 | 0.014* | 0.004 | |
| (0.014) | (0.006) | (0.003) | ||
| Middle Keys | 0.016 | 0.006 | 0.005+ | |
| (0.014) | (0.005) | (0.003) | ||
| Lower Keys | −0.024+ | −0.011* | −0.003 | |
| (0.014) | (0.005) | (0.003) | ||
| Num.Obs. | 344 | 318 | 388 | 147 |
| R2 Marg. | 0.039 | 0.031 | 0.016 | 0.011 |
| R2 Cond. | 0.698 | 0.624 | 0.673 | 0.567 |
| AIC | −967.5 | −1170.4 | −1853.2 | −734.5 |
| BIC | −940.6 | −1144.0 | −1825.5 | −722.5 |
| ICC | 0.7 | 0.6 | 0.7 | 0.6 |
| RMSE | 0.05 | 0.03 | 0.02 | 0.02 |
| + p |
#modelsummary(pH_models, estimate = "p.value")Trends_pH<-ggarrange(In_pH_Modeled+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Mc_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
Of_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
Oc_pH_Modeled + theme(legend.position = "none",
axis.text.x=element_blank(),
axis.title.y=element_blank()),
labels = c("B"),
ncol = 4, nrow = 1, widths =c(1.1, 1, 1, 1))
Trends_pH
TA-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$TA_umol_kg
## W = 0.81838, p-value < 2.2e-16
Inshore
# Inshore TA
hist(Trends.inshore$TA_umol_kg)
shapiro.test(Trends.inshore$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$TA_umol_kg
## W = 0.97801, p-value = 3.91e-05TA_in_0<-lmer(TA_umol_kg~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID), data=Trends.inshore)
#summary(TA_in_0)
anova(TA_in_0) ranova(TA_in_0) step(TA_in_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1945.9 3913.9
## (1 | SiteID) 1 10 -1946.6 3913.2 1.36 1 0.2435
## (1 | Month) 0 9 -2024.9 4067.7 156.49 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 24810 8270 3 326.96 1.7632 0.1540
## Year 2 101 101 1 340.99 0.0214 0.8838
## Sub_region 0 519887 173296 3 331.02 36.8010 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Sub_region + (1 | Month)TA_in<-lmer(TA_umol_kg ~ Sub_region + (1 | Month),
data=Trends.inshore)
summary(TA_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 3916.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4979 -0.5647 0.0543 0.5822 3.3187
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 4279 65.41
## Residual 4709 68.62
## Number of obs: 346, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2375.311 20.710 14.442 114.692 < 2e-16 ***
## Sub_regionUK 3.082 11.147 330.950 0.277 0.78233
## Sub_regionMK -33.793 11.016 330.953 -3.068 0.00234 **
## Sub_regionLK -91.997 10.922 331.046 -8.423 1.13e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.306
## Sub_reginMK -0.310 0.573
## Sub_reginLK -0.313 0.576 0.588 plot(TA_in)
qqnorm(resid(TA_in))
acf(resid(TA_in), main="acf(resid(TA Inshore))") 
TA_in.emmc<-emmeans(TA_in, ~Sub_region)
TA_in.emmc<-multcomp::cld(TA_in.emmc)
TA_in.emmc<-TA_in.emmc[order(TA_in.emmc$Sub_region),]
TA_in.emmcMid channel
hist(Trends.midchanel$TA_umol_kg)
shapiro.test(Trends.midchanel$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$TA_umol_kg
## W = 0.87233, p-value = 1.26e-15TA_mc_0<-lmer(TA_umol_kg~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(TA_mc_0)
anova(TA_mc_0) ranova(TA_mc_0) step(TA_mc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1659.6 3341.2
## (1 | Month) 0 10 -1697.0 3414.0 74.769 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1661.0 3342.1 2.857 1 0.09095 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 6542.6 2180.9 3 295.170 1.1323 0.33623
## Year 2 596.7 596.7 1 304.769 0.3093 0.57854
## Sub_region 0 27458.2 9152.7 3 6.241 4.7458 0.04778 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Sub_region + (1 | Month) + (1 | SiteID)TA_mc<-lmer(TA_umol_kg ~ (1 | Month) + (1|SiteID),
data = Trends.midchanel)
summary(TA_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ (1 | Month) + (1 | SiteID)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3367
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.0951 -0.4479 0.1143 0.5291 3.4663
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 904.3 30.07
## SiteID (Intercept) 248.8 15.77
## Residual 1929.2 43.92
## Number of obs: 320, groups: Month, 12; SiteID, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2362.46 10.34 15.71 228.4 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(TA_mc)
qqnorm(resid(TA_mc))
acf(resid(TA_mc), main="acf(resid(TA Mid Channel))") 
Offshore
hist(Trends.offshore$TA_umol_kg)
shapiro.test(Trends.offshore$TA_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$TA_umol_kg
## W = 0.88004, p-value < 2.2e-16TA_of_0<-lmer(TA_umol_kg~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(TA_of_0)
anova(TA_of_0) ranova(TA_of_0) step(TA_of_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1681.9 3385.8
## (1 | SiteID) 1 10 -1681.9 3383.8 0.000 1 1
## (1 | Month) 0 9 -1712.8 3443.7 61.928 1 3.562e-15 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 193.64 64.55 3 371.60 0.2024 0.89468
## Year 2 3.81 3.81 1 366.89 0.0120 0.91276
## Sub_region 0 2944.96 981.65 3 376.33 3.1051 0.02658 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Sub_region + (1 | Month)TA_of<-lmer(TA_umol_kg ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(TA_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3370.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6937 -0.3550 0.0732 0.5895 3.5239
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 111.4 10.56
## Residual 316.8 17.80
## Number of obs: 391, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2498.26924 1087.51333 366.86056 2.297 0.0222 *
## Year -0.05908 0.53893 366.88694 -0.110 0.9128
## Sub_regionUK 0.27805 2.86697 374.57369 0.097 0.9228
## Sub_regionMK -6.12303 2.83594 374.56360 -2.159 0.0315 *
## Sub_regionLK -4.77981 2.69007 375.14829 -1.777 0.0764 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.007 0.006
## Sub_reginMK -0.007 0.006 0.590
## Sub_reginLK -0.014 0.012 0.624 0.632 plot(TA_of)
qqnorm(resid(TA_of))
acf(resid(TA_of), main="acf(resid(TA Offshore))") 
TA_of.emmc<-emmeans(TA_of, ~Sub_region)
TA_of.emmc<-multcomp::cld(TA_of.emmc)
TA_of.emmc<-TA_of.emmc[order(TA_of.emmc$Sub_region),]
TA_of.emmcOceanic
hist(Trends.ocean$TA_umol_kg)
shapiro.test(Trends.ocean$TA_umol_kg)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$TA_umol_kg
## W = 0.98763, p-value = 0.2082TA_oc_0<-lmer(TA_umol_kg ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(TA_oc_0)
anova(TA_oc_0) ranova(TA_oc_0) step(TA_oc_0)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -553.79 1125.6
## (1 | SiteID) 1 8 -553.79 1123.6 0.000 1 0.9999833
## (1 | Month) 0 7 -560.39 1134.8 13.207 1 0.0002789 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 78.07 39.03 2 133.08 0.3885 0.67885
## Sub_region 2 346.32 173.16 2 134.86 1.7378 0.17982
## Year 0 436.28 436.28 1 134.44 4.3181 0.03961 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## TA_umol_kg ~ Year + (1 | Month)TA_oc<-lmer(TA_umol_kg ~ (1 | Month),
data = Trends.ocean)
summary(TA_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: TA_umol_kg ~ (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1126
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.0590 -0.5199 0.1027 0.5590 2.7345
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 30.24 5.499
## Residual 102.01 10.100
## Number of obs: 149, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2380.537 1.811 10.672 1315 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 plot(TA_oc)
qqnorm(resid(TA_oc))
acf(resid(TA_oc), main="acf(resid(TA_Ocean))")
# TA_oc.emmc<-emmeans(TA_oc, ~Sub_region)
# TA_oc.emmc<-multcomp::cld(TA_oc.emmc)
# TA_oc.emmc<-TA_oc.emmc[order(TA_oc.emmc$Sub_region),]
# TA_oc.emmcModels summary
Complete models
# ANOVAs
# Convert ANOVA results into dataframes allows for easier name manipulation
av.TA_in_0 <- data.frame (anova(TA_in_0))
av.TA_mc_0 <- data.frame (anova(TA_mc_0) )
av.TA_of_0 <- data.frame (anova(TA_of_0))
av.TA_oc_0 <- data.frame (anova(TA_oc_0))
# Putting all into one dataframe/table
anova_TA_0 <- data.frame(cbind
(c("Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region",
"Year", "Sub_region", "Year:Sub_region"),
rbind(av.TA_in_0, av.TA_mc_0,
av.TA_of_0, av.TA_oc_0)))
colnames(anova_TA_0) <- c("", "Sum Sq", "Mean Sq", "N Df" , "D Df",
"F value", "Pr(>F)")
row.names(anova_TA_0) <- NULL
# create HTML table using kableExtra
anova_TA_0 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "TA mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "TA offshore", 7, 9) %>% # groups rows with label
pack_rows(., "TA ocean", 10, 12) | Sum Sq | Mean Sq | N Df | D Df | F value | Pr(>F) | |
|---|---|---|---|---|---|---|
| TA inshore | ||||||
| Year | 2.42 | 2.42 | 1 | 332.03 | 0.00 | 0.98 |
| Sub_region | 25039.93 | 8346.64 | 3 | 320.66 | 1.81 | 0.15 |
| Year:Sub_region | 25059.97 | 8353.32 | 3 | 320.66 | 1.81 | 0.15 |
| TA mid-channel | ||||||
| Year | 1034.52 | 1034.52 | 1 | 302.96 | 0.54 | 0.46 |
| Sub_region | 6543.03 | 2181.01 | 3 | 295.17 | 1.13 | 0.34 |
| Year:Sub_region | 6542.65 | 2180.88 | 3 | 295.17 | 1.13 | 0.34 |
| TA offshore | ||||||
| Year | 15.13 | 15.13 | 1 | 366.42 | 0.05 | 0.83 |
| Sub_region | 194.42 | 64.81 | 3 | 371.64 | 0.20 | 0.89 |
| Year:Sub_region | 193.64 | 64.55 | 3 | 371.64 | 0.20 | 0.89 |
| TA ocean | ||||||
| Year | 427.14 | 427.14 | 1 | 135.62 | 4.25 | 0.04 |
| Sub_region | 77.74 | 38.87 | 2 | 133.07 | 0.39 | 0.68 |
| Year:Sub_region | 78.07 | 39.03 | 2 | 133.07 | 0.39 | 0.68 |
# R-ANOVAs
rav.TA_in_0 <- data.frame (ranova(TA_in_0))
rav.TA_mc_0 <- data.frame (ranova(TA_mc_0) )
rav.TA_of_0 <- data.frame (ranova(TA_of_0))
rav.TA_oc_0 <- data.frame (ranova(TA_oc_0))
# Putting all into one dataframe/table
ranova_TA_0 <- data.frame(cbind
(c("<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)",
"<none>", "(1 | Month)", "(1 | SiteID)"),
rbind(rav.TA_in_0, rav.TA_mc_0,
rav.TA_of_0, rav.TA_oc_0)))
colnames(ranova_TA_0) <- c("", "npar", "logLik", "AIC" , "LRT",
"DF", "Pr(>Chi Sq)")
row.names(ranova_TA_0) <- NULL
# create HTML table using kableExtra
ranova_TA_0 %>% kable("html", digits=2) %>%
kable_styling(bootstrap_options = "striped", full_width = F) %>%
pack_rows(., "TA inshore", 1, 3) %>% # groups rows with label
pack_rows(., "TA mid-channel", 4, 6) %>% # groups rows with label
pack_rows(., "TA offshore", 7, 9) %>% # groups rows with label
pack_rows(., "TA ocean", 10, 12)| npar | logLik | AIC | LRT | DF | Pr(>Chi Sq) | |
|---|---|---|---|---|---|---|
| TA inshore | ||||||
| <none> | 11 | -1945.93 | 3913.87 | |||
| (1 | Month) | 10 | -2024.86 | 4069.72 | 157.85 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1946.61 | 3913.23 | 1.36 | 1 | 0.24 |
| TA mid-channel | ||||||
| <none> | 11 | -1659.60 | 3341.20 | |||
| (1 | Month) | 10 | -1696.99 | 3413.97 | 74.77 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1661.03 | 3342.06 | 2.86 | 1 | 0.09 |
| TA offshore | ||||||
| <none> | 11 | -1681.89 | 3385.78 | |||
| (1 | Month) | 10 | -1712.85 | 3445.71 | 61.93 | 1 | 0.00 |
| (1 | SiteID) | 10 | -1681.89 | 3383.78 | 0.00 | 1 | 1.00 |
| TA ocean | ||||||
| <none> | 9 | -553.79 | 1125.58 | |||
| (1 | Month) | 8 | -560.39 | 1136.79 | 13.21 | 1 | 0.00 |
| (1 | SiteID) | 8 | -553.79 | 1123.58 | 0.00 | 1 | 1.00 |
TA_models_0<-list("Inshore" = TA_in_0,
"Mid channel" = TA_mc_0,
"Offshore" = TA_of_0,
"Ocean" = TA_oc_0)
modelsummary(TA_models_0, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'TA model outputs (all factors)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −1.960 | −1.660 | −0.782 | 1.276 |
| (4.183) | (2.736) | (1.101) | (0.943) | |
| Intercept | 6329.685 | 5725.313 | 3957.018+ | −192.795 |
| (8441.085) | (5520.064) | (2222.111) | (1902.437) | |
| Upper Keys | 300.228 | 1795.966 | −1817.493 | |
| (10704.748) | (7610.829) | (2764.945) | ||
| Middle Keys | −17544.634+ | −8096.830 | −1984.851 | 158.856 |
| (10383.855) | (6732.092) | (2738.961) | (2205.279) | |
| Lower Keys | 1722.170 | 1762.614 | −1551.341 | 1520.525 |
| (10413.534) | (6768.064) | (2600.459) | (2215.788) | |
| Num.Obs. | 346 | 320 | 391 | 149 |
| R2 Marg. | 0.152 | 0.070 | 0.025 | 0.063 |
| R2 Cond. | 0.566 | 0.396 | ||
| AIC | 3913.9 | 3341.2 | 3385.8 | 1125.6 |
| BIC | 3956.2 | 3382.7 | 3429.4 | 1152.6 |
| ICC | 0.5 | 0.4 | ||
| RMSE | 65.79 | 42.39 | 17.44 | 9.54 |
| + p |
#modelsummary(TA_models, estimate = "p.value")Factors removed models
TA_models<-list("Inshore" = TA_in,
"Mid channel" = TA_mc,
"Offshore" = TA_of,
"Ocean" = TA_oc)
modelsummary(TA_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'TA model outputs (dropped facators)',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | −0.059 | |||
| (0.539) | ||||
| Intercept | 2375.311*** | 2362.465*** | 2498.269* | 2380.537*** |
| (20.710) | (10.342) | (1087.513) | (1.811) | |
| Upper Keys | 3.082 | 0.278 | ||
| (11.147) | (2.867) | |||
| Middle Keys | −33.793** | −6.123* | ||
| (11.016) | (2.836) | |||
| Lower Keys | −91.997*** | −4.780+ | ||
| (10.922) | (2.690) | |||
| Num.Obs. | 346 | 320 | 391 | 149 |
| R2 Marg. | 0.146 | 0.000 | 0.018 | 0.000 |
| R2 Cond. | 0.552 | 0.374 | 0.273 | 0.229 |
| AIC | 3928.5 | 3375.0 | 3384.5 | 1132.0 |
| BIC | 3951.6 | 3390.1 | 3412.3 | 1141.1 |
| ICC | 0.5 | 0.4 | 0.3 | 0.2 |
| RMSE | 67.16 | 42.65 | 17.46 | 9.77 |
| + p |
#modelsummary(TA_models, estimate = "p.value")DIC-All
##
## Shapiro-Wilk normality test
##
## data: Trends.data$DIC_umol_kg
## W = 0.89265, p-value < 2.2e-16
Inshore
hist(Trends.inshore$DIC_umol_kg)
shapiro.test(Trends.inshore$DIC_umol_kg)# Normality NOT rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$DIC_umol_kg
## W = 0.99743, p-value = 0.8723# Inshore DIC
DIC_in<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(DIC_in)
anova(DIC_in) ranova(DIC_in) step(DIC_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1944.8 3911.6
## (1 | Month) 0 10 -2065.7 4151.4 241.828 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1947.4 3914.7 5.107 1 0.02382 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 25840 8613.2 3 318.54 1.7970 0.14761
## Year 2 419 419.5 1 331.50 0.0869 0.76838
## Sub_region 0 89217 29739.1 3 7.19 6.1767 0.02126 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month) + (1 | SiteID)DIC_in<-lmer(DIC_umol_kg ~ Year + Sub_region +
(1|Month) + (1|SiteID), data=Trends.inshore)
summary(DIC_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3909.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4187 -0.6488 -0.0022 0.6496 2.7936
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6878.9 82.94
## SiteID (Intercept) 261.7 16.18
## Residual 4829.2 69.49
## Number of obs: 344, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 675.9450 4678.1870 331.5171 0.144 0.88520
## Year 0.6833 2.3182 331.5036 0.295 0.76838
## Sub_regionUK -16.4631 18.5970 7.1538 -0.885 0.40479
## Sub_regionMK -46.2885 18.5087 7.0228 -2.501 0.04083 *
## Sub_regionLK -70.7519 18.4783 6.9754 -3.829 0.00651 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.026 0.024
## Sub_reginMK -0.013 0.010 0.590
## Sub_reginLK -0.023 0.021 0.591 0.595 plot(DIC_in)
qqnorm(resid(DIC_in))
acf(resid(DIC_in), main="acf(resid(DIC Inshore))")
DIC_in.emmc<-emmeans(DIC_in, ~Sub_region+Year)
DIC_in.emmc<-multcomp::cld(DIC_in.emmc)
DIC_in.emmc<-DIC_in.emmc[order(DIC_in.emmc$Sub_region),]
DIC_in.emmcMid channel
hist(Trends.midchanel$DIC_umol_kg)
shapiro.test(Trends.midchanel$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$DIC_umol_kg
## W = 0.93354, p-value = 1.006e-10DIC_mc<-lmer(DIC_umol_kg~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(DIC_mc)
anova(DIC_mc) ranova(DIC_mc) step(DIC_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1658.0 3338.0
## (1 | SiteID) 1 10 -1658.7 3337.3 1.392 1 0.2381
## (1 | Month) 0 9 -1723.8 3465.6 130.301 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 8969.4 2989.8 3 298.83 1.4612 0.225205
## Year 2 2116.7 2116.7 1 312.94 1.0294 0.311081
## Sub_region 0 29687.4 9895.8 3 303.09 4.8018 0.002777 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month)DIC_mc<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data = Trends.midchanel)
summary(DIC_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3334
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3673 -0.4419 0.0603 0.5355 3.9086
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1701 41.24
## Residual 2056 45.35
## Number of obs: 318, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5217.407 3124.726 312.935 1.670 0.095975 .
## Year -1.571 1.548 312.936 -1.015 0.311081
## Sub_regionUK -8.845 8.252 301.917 -1.072 0.284641
## Sub_regionMK -21.745 7.279 301.853 -2.987 0.003045 **
## Sub_regionLK -24.605 7.267 301.979 -3.386 0.000803 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.019 0.018
## Sub_reginMK -0.004 0.003 0.528
## Sub_reginLK -0.025 0.024 0.531 0.602 plot(DIC_mc)
qqnorm(resid(DIC_mc))
acf(resid(DIC_mc), main="acf(resid(DIC Mid Channel))") 
DIC_mc.emmc<-emmeans(DIC_mc, ~Sub_region+Year)
DIC_mc.emmc<-multcomp::cld(DIC_mc.emmc)
DIC_mc.emmc<-DIC_mc.emmc[order(DIC_mc.emmc$Sub_region),]
DIC_mc.emmcOffshore
hist(Trends.offshore$DIC_umol_kg)
shapiro.test(Trends.offshore$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$DIC_umol_kg
## W = 0.92737, p-value = 8.011e-13DIC_of<-lmer(DIC_umol_kg~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(DIC_of)
anova(DIC_of) ranova(DIC_of) step(DIC_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1695.8 3413.7
## (1 | SiteID) 1 10 -1695.8 3411.7 0.00 1 1
## (1 | Month) 0 9 -1757.9 3533.8 124.11 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 878.8 292.95 3 370.59 0.8509 0.466791
## Year 2 406.7 406.66 1 384.15 1.1825 0.277525
## Sub_region 0 4526.5 1508.84 3 374.98 4.3971 0.004676 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Sub_region + (1 | Month)DIC_of<-lmer(DIC_umol_kg ~ Year + Sub_region + (1 | Month),
data =Trends.offshore)
summary(DIC_of)## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3400.6
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.8843 -0.4329 0.1397 0.6130 2.4102
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 243.8 15.61
## Residual 343.9 18.54
## Number of obs: 390, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 792.3190 1154.5069 384.1392 0.686 0.49295
## Year 0.6221 0.5721 384.1451 1.087 0.27753
## Sub_regionUK -2.9722 2.9870 373.5600 -0.995 0.32037
## Sub_regionMK -9.1133 2.9547 373.5576 -3.084 0.00219 **
## Sub_regionLK -1.3305 2.8028 373.7602 -0.475 0.63528
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.007 0.006
## Sub_reginMK -0.007 0.005 0.590
## Sub_reginLK -0.020 0.019 0.624 0.632 plot(DIC_of)
qqnorm(resid(DIC_of))
acf(resid(DIC_of), main="acf(resid(DIC Offshore))") 
DIC_of.emmc<-emmeans(DIC_of, ~Sub_region+Year)
DIC_of.emmc<-multcomp::cld(DIC_of.emmc)
DIC_of.emmc<-DIC_of.emmc[order(DIC_of.emmc$Sub_region),]
DIC_of.emmcOceanic
hist(Trends.ocean$DIC_umol_kg)
shapiro.test(Trends.ocean$DIC_umol_kg)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.ocean$DIC_umol_kg
## W = 0.98641, p-value = 0.1591DIC_oc<-lmer(DIC_umol_kg ~ Year * Sub_region +
(1 | Month)+ (1|SiteID),
data=Trends.ocean)
#summary(DIC_oc)
anova(DIC_oc) ranova(DIC_oc) step(DIC_oc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 9 -579.29 1176.6
## (1 | SiteID) 1 8 -579.29 1174.6 0.000 1 1
## (1 | Month) 0 7 -599.53 1213.1 40.494 1 1.972e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 166.40 83.20 2 129.53 0.5593 0.5730028
## Sub_region 2 121.96 60.98 2 131.46 0.4125 0.6628785
## Year 0 1859.81 1859.81 1 144.81 12.6915 0.0004977 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## DIC_umol_kg ~ Year + (1 | Month)DIC_oc<-lmer(DIC_umol_kg ~ Year+ (1 | Month), data = Trends.ocean)
summary(DIC_oc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: DIC_umol_kg ~ Year + (1 | Month)
## Data: Trends.ocean
##
## REML criterion at convergence: 1171.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.4190 -0.4733 0.1114 0.6111 2.2162
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 125.5 11.20
## Residual 146.5 12.11
## Number of obs: 147, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -2168.1995 1182.8873 144.8121 -1.833 0.068860 .
## Year 2.0884 0.5862 144.8109 3.563 0.000498 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr)
## Year -1.000 plot(DIC_oc)
qqnorm(resid(DIC_oc))
acf(resid(DIC_oc), main="acf(resid(DIC open))")
DIC_oc.emmc<-emmeans(DIC_oc, ~Year)
DIC_oc.emmc<-multcomp::cld(DIC_oc.emmc)
#DIC_oc.emmc<-DIC_oc.emmc[order(DIC_oc.emmc$Sub_region),]
DIC_oc.emmcModels summary
DIC_models<-list("Inshore" = DIC_in,
"Mid channel" = DIC_mc,
"Offshore" = DIC_of,
"Ocean" = DIC_oc)
modelsummary(DIC_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'DIC model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | Ocean | |
|---|---|---|---|---|
| Year | 0.683 | −1.571 | 0.622 | 2.088*** |
| (2.318) | (1.548) | (0.572) | (0.586) | |
| Intercept | 675.945 | 5217.407+ | 792.319 | −2168.199+ |
| (4678.187) | (3124.726) | (1154.507) | (1182.887) | |
| Upper Keys | −16.463 | −8.845 | −2.972 | |
| (18.597) | (8.252) | (2.987) | ||
| Middle Keys | −46.289* | −21.745** | −9.113** | |
| (18.509) | (7.279) | (2.955) | ||
| Lower Keys | −70.752*** | −24.605*** | −1.330 | |
| (18.478) | (7.267) | (2.803) | ||
| Num.Obs. | 344 | 318 | 390 | 147 |
| R2 Marg. | 0.057 | 0.027 | 0.022 | 0.067 |
| R2 Cond. | 0.619 | 0.468 | 0.428 | 0.498 |
| AIC | 3925.6 | 3348.0 | 3414.6 | 1179.9 |
| BIC | 3956.3 | 3374.4 | 3442.4 | 1191.9 |
| ICC | 0.6 | 0.5 | 0.4 | 0.5 |
| RMSE | 67.43 | 44.23 | 18.17 | 11.61 |
| + p |
#modelsummary(DIC_models, estimate = "p.value")dTA-All
Long_plot2<- ggplot(Reef.Trends.data) +
MyTheme +
#scale_shape_manual(values=c(24, 25, 21))+
#guides(fill=guide_legend(override.aes=list(shape=21)))+
theme(axis.title.x = element_blank())
##
## Shapiro-Wilk normality test
##
## data: Reef.Trends.data$dTA_MY
## W = 0.82636, p-value < 2.2e-16
Inshore
# Inshore dTA
hist(Trends.inshore$dTA_MY)
shapiro.test(Trends.inshore$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$dTA_MY
## W = 0.97537, p-value = 1.479e-05dTA_in<-lmer(dTA_MY ~ Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(dTA_in)
anova(dTA_in) ranova(dTA_in) step(dTA_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1907.7 3837.3
## (1 | SiteID) 1 10 -1908.4 3836.8 1.494 1 0.2215
## (1 | Month) 0 9 -1988.5 3995.0 160.211 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 24316 8105 3 320.99 1.7756 0.1517
## Year 2 514 514 1 334.99 0.1118 0.7383
## Sub_region 0 521831 173944 3 325.00 37.9152 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ Sub_region + (1 | Month)dTA_in<-lmer(dTA_MY ~ Sub_region + (1 | Month),
data=Trends.inshore)
summary(dTA_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ Sub_region + (1 | Month)
## Data: Trends.inshore
##
## REML criterion at convergence: 3840.1
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.5210 -0.5933 0.0214 0.5820 3.2516
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 4316 65.70
## Residual 4588 67.73
## Number of obs: 340, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -4.420 20.788 14.432 -0.213 0.83458
## Sub_regionUK 2.995 11.181 324.943 0.268 0.78898
## Sub_regionMK -34.377 10.994 324.943 -3.127 0.00193 **
## Sub_regionLK -92.729 10.874 325.016 -8.527 5.74e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.305
## Sub_reginMK -0.311 0.575
## Sub_reginLK -0.314 0.580 0.594 plot(dTA_in)
qqnorm(resid(dTA_in))
acf(resid(dTA_in), main="acf(resid(dTA Inshore))")
dTA_in.emmc<-emmeans(dTA_in, ~Sub_region)
dTA_in.emmc<-multcomp::cld(dTA_in.emmc)
dTA_in.emmc<-dTA_in.emmc[order(dTA_in.emmc$Sub_region),]
dTA_in.emmcMid channel
hist(Trends.midchanel$dTA_MY)
shapiro.test(Trends.midchanel$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$dTA_MY
## W = 0.84625, p-value < 2.2e-16dTA_mc<-lmer(dTA_MY~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID),
data=Trends.midchanel)
#summary(dTA_mc)
anova(dTA_mc) ranova(dTA_mc) step(dTA_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1634.9 3291.8
## (1 | Month) 0 10 -1675.4 3370.9 81.063 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1636.5 3293.1 3.278 1 0.07021 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 6569.9 2190.0 3 292.08 1.2103 0.30618
## Year 2 3291.7 3291.7 1 303.51 1.8147 0.17895
## Sub_region 0 26984.9 8995.0 3 6.28 4.9261 0.04385 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ Sub_region + (1 | Month) + (1 | SiteID)dTA_mc<-lmer(dTA_MY ~ (1 | Month) + (1|SiteID),
data = Trends.midchanel)
summary(dTA_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ (1 | Month) + (1 | SiteID)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3319.3
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.9404 -0.4288 0.1101 0.5806 3.3889
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 883.6 29.73
## SiteID (Intercept) 256.2 16.01
## Residual 1826.3 42.74
## Number of obs: 317, groups: Month, 12; SiteID, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -17.91 10.28 15.93 -1.743 0.101 plot(dTA_mc)
qqnorm(resid(dTA_mc))
acf(resid(dTA_mc), main="acf(resid(dTA Mid Channel))") 
Offshore
hist(Trends.offshore$dTA_MY)
shapiro.test(Trends.offshore$dTA_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$dTA_MY
## W = 0.82187, p-value < 2.2e-16dTA_of<-lmer(dTA_MY~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(dTA_of)
anova(dTA_of) ranova(dTA_of) step(dTA_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1614.0 3249.9
## (1 | SiteID) 1 10 -1614.0 3247.9 0.000 1 1
## (1 | Month) 0 9 -1638.7 3295.3 49.396 1 2.091e-12 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 201.07 67.02 3 366.30 0.2673 0.84897
## Year 0 1160.22 1160.22 1 350.84 4.6547 0.03165 *
## Sub_region 0 2796.99 932.33 3 369.62 3.7404 0.01136 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dTA_MY ~ Year + Sub_region + (1 | Month)dTA_of<-lmer(dTA_MY ~ Sub_region + (1 | Month),
data =Trends.offshore)
summary(dTA_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dTA_MY ~ Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3238.8
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -5.6298 -0.3162 0.0951 0.5352 3.7473
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 56.29 7.503
## Residual 253.19 15.912
## Number of obs: 386, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) -1.8949 2.9537 27.7282 -0.642 0.5264
## Sub_regionUK 0.6773 2.5912 370.9652 0.261 0.7939
## Sub_regionMK -5.7043 2.5691 370.9841 -2.220 0.0270 *
## Sub_regionLK -4.4724 2.4279 371.6955 -1.842 0.0663 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.516
## Sub_reginMK -0.523 0.594
## Sub_reginLK -0.554 0.629 0.637 plot(dTA_of)
qqnorm(resid(dTA_of))
acf(resid(dTA_of), main="acf(resid(dTA Offshore))") 
dTA_of.emmc<-emmeans(dTA_of, ~Sub_region)
dTA_of.emmc<-multcomp::cld(dTA_of.emmc)
dTA_of.emmc<-dTA_of.emmc[order(dTA_of.emmc$Sub_region),]
dTA_of.emmcModels summary
dTA_models<-list("Inshore" = dTA_in,
"Mid channel" = dTA_mc,
"Offshore" = dTA_of)
modelsummary(dTA_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'dTA model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | |
|---|---|---|---|
| Intercept | −4.420 | −17.914+ | −1.895 |
| (20.788) | (10.280) | (2.954) | |
| Upper Keys | 2.995 | 0.677 | |
| (11.181) | (2.591) | ||
| Middle Keys | −34.377** | −5.704* | |
| (10.994) | (2.569) | ||
| Lower Keys | −92.729*** | −4.472+ | |
| (10.874) | (2.428) | ||
| Num.Obs. | 340 | 317 | 386 |
| R2 Marg. | 0.149 | 0.000 | 0.023 |
| R2 Cond. | 0.562 | 0.384 | 0.201 |
| AIC | 3852.1 | 3327.3 | 3250.8 |
| BIC | 3875.1 | 3342.3 | 3274.6 |
| ICC | 0.5 | 0.4 | 0.2 |
| RMSE | 66.27 | 41.47 | 15.63 |
| + p |
#modelsummary(dTA_models, estimate = "p.value")dDIC-All
##
## Shapiro-Wilk normality test
##
## data: Reef.Trends.data$dDIC_MY
## W = 0.87871, p-value < 2.2e-16
Inshore
# Inshore dDIC
hist(Trends.inshore$dDIC_MY)
shapiro.test(Trends.inshore$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.inshore$dDIC_MY
## W = 0.99469, p-value = 0.2936dDIC_in<-lmer(dDIC_MY~Year * Sub_region + (1|Month)+
#(1|Zone)+
(1|SiteID),
data=Trends.inshore)
#summary(dDIC_in)
anova(dDIC_in) ranova(dDIC_in) step(dDIC_in)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1904.2 3830.5
## (1 | Month) 0 10 -2019.6 4059.1 230.620 1 < 2e-16 ***
## (1 | SiteID) 0 10 -1907.0 3834.1 5.592 1 0.01804 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 25533 8511.1 3 312.67 1.8460 0.13874
## Year 2 1925 1925.4 1 325.59 0.4142 0.52028
## Sub_region 0 88730 29576.7 3 7.24 6.3731 0.01943 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Sub_region + (1 | Month) + (1 | SiteID)dDIC_in<-lmer(dDIC_MY ~ Sub_region + (1 | Month)+ (1 | SiteID),
data=Trends.inshore)
summary(dDIC_in) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Sub_region + (1 | Month) + (1 | SiteID)
## Data: Trends.inshore
##
## REML criterion at convergence: 3832.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -3.3028 -0.6819 0.0077 0.6656 2.8679
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 6311.1 79.44
## SiteID (Intercept) 269.4 16.41
## Residual 4640.9 68.12
## Number of obs: 338, groups: Month, 12; SiteID, 11
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 9.859 27.092 16.462 0.364 0.72056
## Sub_regionUK -16.203 18.739 7.264 -0.865 0.41486
## Sub_regionMK -46.709 18.623 7.089 -2.508 0.04009 *
## Sub_regionLK -72.041 18.574 7.014 -3.879 0.00604 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.407
## Sub_reginMK -0.410 0.591
## Sub_reginLK -0.410 0.592 0.597 plot(dDIC_in)
qqnorm(resid(dDIC_in))
acf(resid(dDIC_in), main="acf(resid(dDIC Inshore))") 
dDIC_in.emmc<-emmeans(dDIC_in, ~Sub_region)
dDIC_in.emmc<-multcomp::cld(dDIC_in.emmc)
dDIC_in.emmc<-dDIC_in.emmc[order(dDIC_in.emmc$Sub_region),]
dDIC_in.emmcMid channel
hist(Trends.midchanel$dDIC_MY)
shapiro.test(Trends.midchanel$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.midchanel$dDIC_MY
## W = 0.90543, p-value = 3.815e-13dDIC_mc<-lmer(dDIC_MY~Year * Sub_region + (1|Month) +
#(1|Zone)+
(1|SiteID), data=Trends.midchanel)
#summary(dDIC_mc)
anova(dDIC_mc) ranova(dDIC_mc) step(dDIC_mc)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1630.0 3282.0
## (1 | SiteID) 1 10 -1630.9 3281.9 1.85 1 0.1738
## (1 | Month) 0 9 -1689.2 3396.3 116.45 1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 9222 3074.2 3 296.02 1.6147 0.186019
## Year 0 11873 11873.1 1 309.30 6.1955 0.013333 *
## Sub_region 0 31740 10580.0 3 299.10 5.5207 0.001058 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Year + Sub_region + (1 | Month)dDIC_mc<-lmer(dDIC_MY ~ Sub_region + (1 | Month),
data = Trends.midchanel)
summary(dDIC_mc) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Sub_region + (1 | Month)
## Data: Trends.midchanel
##
## REML criterion at convergence: 3287.5
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -4.3642 -0.4770 0.0429 0.5740 3.8569
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 1200 34.63
## Residual 1956 44.22
## Number of obs: 315, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2.059 11.481 16.715 0.179 0.859861
## Sub_regionUK -9.921 8.097 300.141 -1.225 0.221439
## Sub_regionMK -22.716 7.179 300.116 -3.164 0.001715 **
## Sub_regionLK -25.558 7.148 300.219 -3.576 0.000407 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Sb_rUK Sb_rMK
## Sub_reginUK -0.334
## Sub_reginMK -0.379 0.534
## Sub_reginLK -0.380 0.537 0.608 plot(dDIC_mc) 
qqnorm(resid(dDIC_mc))
acf(resid(dDIC_mc), main="acf(resid(dDIC Mid Channel))") 
dDIC_mc.emmc<-emmeans(dDIC_mc, ~Sub_region)
dDIC_mc.emmc<-multcomp::cld(dDIC_mc.emmc)
dDIC_mc.emmc<-dDIC_mc.emmc[order(dDIC_mc.emmc$Sub_region),]
dDIC_mc.emmcOffshore
hist(Trends.offshore$dDIC_MY)
shapiro.test(Trends.offshore$dDIC_MY)# Normality rejected##
## Shapiro-Wilk normality test
##
## data: Trends.offshore$dDIC_MY
## W = 0.78582, p-value < 2.2e-16dDIC_of<-lmer(dDIC_MY~Year * Sub_region +
(1|Month) + (1|SiteID),
data=Trends.offshore)
#summary(dDIC_of)
anova(dDIC_of) ranova(dDIC_of) step(dDIC_of)## Backward reduced random-effect table:
##
## Eliminated npar logLik AIC LRT Df Pr(>Chisq)
## <none> 11 -1603.4 3228.9
## (1 | SiteID) 1 10 -1603.4 3226.9 0.000 1 1
## (1 | Month) 0 9 -1616.4 3250.8 25.897 1 3.602e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Backward reduced fixed-effect table:
## Degrees of freedom method: Satterthwaite
##
## Eliminated Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## Year:Sub_region 1 974.6 324.87 3 366.36 1.3149 0.269227
## Year 0 2077.2 2077.25 1 280.89 8.3858 0.004079 **
## Sub_region 0 3751.7 1250.57 3 369.77 5.0485 0.001937 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Model found:
## dDIC_MY ~ Year + Sub_region + (1 | Month)dDIC_of<-lmer(dDIC_MY ~ Year+ Sub_region + (1 | Month),
data =Trends.offshore)
summary(dDIC_of) ## Linear mixed model fit by REML. t-tests use Satterthwaite's method [
## lmerModLmerTest]
## Formula: dDIC_MY ~ Year + Sub_region + (1 | Month)
## Data: Trends.offshore
##
## REML criterion at convergence: 3216.2
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -6.2028 -0.3388 0.1296 0.5453 3.7043
##
## Random effects:
## Groups Name Variance Std.Dev.
## Month (Intercept) 33.24 5.765
## Residual 247.71 15.739
## Number of obs: 385, groups: Month, 12
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 2687.6949 927.6272 280.8380 2.897 0.004059 **
## Year -1.3312 0.4597 280.8893 -2.896 0.004079 **
## Sub_regionUK -2.7375 2.5630 369.2943 -1.068 0.286173
## Sub_regionMK -8.6035 2.5411 369.3350 -3.386 0.000786 ***
## Sub_regionLK -1.7183 2.4011 370.0752 -0.716 0.474676
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Correlation of Fixed Effects:
## (Intr) Year Sb_rUK Sb_rMK
## Year -1.000
## Sub_reginUK -0.008 0.006
## Sub_reginMK -0.007 0.005 0.594
## Sub_reginLK -0.019 0.017 0.630 0.636 plot(dDIC_of)
qqnorm(resid(dDIC_of))
acf(resid(dDIC_of), main="acf(resid(dDIC Offshore))") 
dDIC_of.emmc<-emmeans(dDIC_of, ~Sub_region+Year)
dDIC_of.emmc<-multcomp::cld(dDIC_of.emmc)
dDIC_of.emmc<-dDIC_of.emmc[order(dDIC_of.emmc$Sub_region),]
dDIC_of.emmcModels summary
dDIC_models<-list("Inshore" = dDIC_in,
"Mid channel" = dDIC_mc,
"Offshore" = dDIC_of)
modelsummary(dDIC_models, stars = TRUE,
#statistic = c('std.error', 'p.value', 'conf.int'),
title = 'dDIC model outputs',
coef_map=factor_order)| Inshore | Mid channel | Offshore | |
|---|---|---|---|
| Year | −1.331** | ||
| (0.460) | |||
| Intercept | 9.859 | 2.059 | 2687.695** |
| (27.092) | (11.481) | (927.627) | |
| Upper Keys | −16.203 | −9.921 | −2.738 |
| (18.739) | (8.097) | (2.563) | |
| Middle Keys | −46.709* | −22.716** | −8.603*** |
| (18.623) | (7.179) | (2.541) | |
| Lower Keys | −72.041*** | −25.558*** | −1.718 |
| (18.574) | (7.148) | (2.401) | |
| Num.Obs. | 338 | 315 | 385 |
| R2 Marg. | 0.063 | 0.031 | 0.059 |
| R2 Cond. | 0.612 | 0.399 | 0.171 |
| AIC | 3846.4 | 3299.5 | 3230.2 |
| BIC | 3873.2 | 3322.1 | 3257.9 |
| ICC | 0.6 | 0.4 | 0.1 |
| RMSE | 66.16 | 43.21 | 15.46 |
| + p |
#modelsummary(dDIC_models, estimate = "p.value")Figure 2: Trends
Raw data lm
Trends_H<-ggarrange(Trends_a+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Trends_b+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
Trends_c + theme(legend.position = "bottom",
strip.text.x = element_blank()),
labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(0.9, 0.9, 1.2))
Trends_H
Trends_H2<-ggarrange(Trends_a2+ theme(axis.title.x=element_blank(),
axis.text.x=element_blank(),
legend.position = "none"),
Trends_b2+ theme(legend.position = "none",
axis.title.x=element_blank(),
axis.text.x=element_blank(),
strip.text.x = element_blank()),
Trends_c2 + theme(legend.position = "bottom",
strip.text.x = element_blank()),
labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(0.9, 0.9, 1.2))
Trends_H2
TrendsV<-ggarrange(Trends_a+
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
Trends_b+
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
Trends_c +
facet_grid(Zone~., scales = "free_y")+
theme(legend.position = "none",
panel.spacing = unit(0.6, "cm")),
labels = c("A", "B", "C"),
ncol = 3, nrow = 1)
#TrendsV
#ggsave(file="FLK_results/Fig_2_Trends.svg", plot=TrendsH, dpi = 300, width=7.5, height=7)Figure 2: Ocean acidification trends in the Florida Reef Tract. (A) The partial pressure of the CO2, (B) pH, and (C) Aragonite saturation state (Ωar). The colors represent the biogeographical regions and the panels separate the locations by shelf zone. Text on the top of each panel shows the coefficient of change in the parameter per year (for significant trends) and the significance of the region in the model. Note the different scales between the top and bottom panels.
Trends_Modeled<-ggarrange(
Trends_pCO2,
Trends_pH,
Trends_Omega,
#labels = c("A", "B", "C"),
ncol = 1, nrow = 3, heights = c(1.1, 0.9, 1.1))
Trends_Modeled
#ggsave(file="FLK_results/Fig_2_Trends.svg", plot=Trends_Modeled, dpi = 300, width=7.5, height=7)
#ggsave(file="FLK_results/Fig_2_Trends_ICRS.svg", plot=Trends_Modeled, dpi = 300, width=9, height=5)Figure 2: Ocean acidification trends in the Florida Reef Tract. (A) The partial pressure of the CO2, (B) pH, and (C) Aragonite saturation state (Ωar). The colors represent the biogeographical regions and the panels separate the locations by shelf zone. Text on the top of each panel shows the coefficient of change in the parameter per year (for significant trends). Solid lines represent models with significant trends (year<0.05). Multiple coloured lines are depicted for models where region was significant (region p<0.05). Dashed lines represent the intercepts of models with non significant trends (year p>0.05).
7. PCA
#str(Month_Stats)
PCA_variables<-c("DIC_mean", "TA_mean", "pCO2_mean", "Ara_mean",
"pH_mean", "Tem_mean","Sal_mean", "Season",
"Zone", "Sub_region", "Month2")
PCA.data<-select(Month_Stats, PCA_variables)
Name<-paste(Month_Stats$Zone, Month_Stats$Sub_region,
Month_Stats$Month, sep = "_")
rownames(PCA.data) <- (Name)
PCA_var.names<-c("DIC", "TA", "pCO2", "Ara",
"pH", "Temp","Sal", "Season",
"Zone", "Sub_region", "Month")
colnames(PCA.data) <- (PCA_var.names)
#ggpairs(PCA.data)head(PCA.data)library(vegan)
#library(autoplot)
library(ggfortify)
CC.rda1 <- rda(PCA.data[, -(6:11)], scale=TRUE)
CC.rda2 <- prcomp(PCA.data[, -(6:11)], scale=TRUE)
summary(CC.rda1)##
## Call:
## rda(X = PCA.data[, -(6:11)], scale = TRUE)
##
## Partitioning of correlations:
## Inertia Proportion
## Total 5 1
## Unconstrained 5 1
##
## Eigenvalues, and their contribution to the correlations
##
## Importance of components:
## PC1 PC2 PC3 PC4 PC5
## Eigenvalue 2.525 1.9755 0.49029 0.007685 0.0012795
## Proportion Explained 0.505 0.3951 0.09806 0.001537 0.0002559
## Cumulative Proportion 0.505 0.9001 0.99821 0.999744 1.0000000
##
## Scaling 2 for species and site scores
## * Species are scaled proportional to eigenvalues
## * Sites are unscaled: weighted dispersion equal on all dimensions
## * General scaling constant of scores: 5.469602
##
##
## Species scores
##
## PC1 PC2 PC3 PC4 PC5
## DIC -0.1277 2.4406 -0.08088 -0.009083 -0.061307
## TA 0.9923 2.1598 -0.57496 -0.013337 0.056949
## pCO2 -2.3514 0.1789 -0.63231 0.150261 0.005565
## Ara 1.7375 -1.0641 -1.35334 0.003904 -0.024707
## pH 2.3581 0.1857 0.60424 0.152077 -0.003531
##
##
## Site scores (weighted sums of species scores)
##
## PC1 PC2 PC3 PC4 PC5
## Inshore_BB_Jan -0.101692 0.764688 0.449665 -0.0869779 -1.467e-01
## Inshore_BB_Feb 0.411329 0.473426 0.348590 0.2083096 -2.794e-01
## Inshore_BB_Mar 0.490212 0.347346 0.026759 0.2805043 -5.204e-02
## Inshore_BB_Apr 0.736874 -0.492056 0.076297 0.5869598 1.949e-01
## Inshore_BB_May 0.662231 -0.367179 -0.164388 0.4705262 5.305e-02
## Inshore_BB_Jun -0.015294 -0.859711 0.493279 -0.3711948 -1.913e-01
## Inshore_BB_Jul -0.512410 -0.337088 -0.162303 -0.1745752 4.554e-01
## Inshore_BB_Aug -0.509657 -0.223719 -0.492129 0.6682418 2.631e-01
## Inshore_BB_Sep -1.120955 0.327362 -0.325952 1.9065707 -4.660e-02
## Inshore_BB_Oct -1.140928 0.678314 -0.352180 1.6677999 2.892e-01
## Inshore_BB_Nov -0.399655 0.668559 -0.038580 0.1471773 2.220e-01
## Inshore_BB_Dec -0.139179 0.648269 0.371045 -0.0825210 -1.677e-01
## Inshore_UK_Jan 0.103639 0.908575 0.328148 0.1084290 -3.095e-02
## Inshore_UK_Feb 0.685704 0.165954 0.327089 0.6422276 -2.137e-01
## Inshore_UK_Mar 0.613208 0.190233 -0.016753 0.3949519 3.777e-01
## Inshore_UK_Apr 1.256829 -1.048020 -0.084817 1.9471954 3.566e-01
## Inshore_UK_May 1.029433 -0.896480 -0.128602 1.0829165 4.432e-01
## Inshore_UK_Jun 0.306790 -0.506137 -0.156907 -0.2447972 3.406e-02
## Inshore_UK_Jul -0.215208 -0.283626 -0.464902 -0.1609288 1.689e-01
## Inshore_UK_Aug 0.145470 -0.371549 -0.804241 -0.3155772 7.423e-02
## Inshore_UK_Sep -0.946748 -0.098190 -0.427248 1.0481384 -3.515e-02
## Inshore_UK_Oct -1.082722 0.478424 -0.300201 1.6129185 2.203e-01
## Inshore_UK_Nov -0.812724 1.027718 -0.216739 1.2106072 8.413e-01
## Inshore_UK_Dec -0.172923 0.943165 0.105962 0.0764009 3.233e-01
## Inshore_MK_Jan -0.086807 0.956835 0.253283 -0.0444325 1.920e-01
## Inshore_MK_Feb 0.807929 0.499034 0.006600 0.9752010 -1.052e+00
## Inshore_MK_Mar 0.491007 0.248469 -0.043740 0.1852552 4.994e-01
## Inshore_MK_Apr 0.967710 -0.664034 -0.077765 1.1067829 3.839e-01
## Inshore_MK_May 0.982311 -1.008389 0.002033 1.1519032 5.411e-01
## Inshore_MK_Jun 0.182232 -1.065783 0.317326 -0.0755431 -1.900e-02
## Inshore_MK_Jul -0.691859 -1.148737 0.627582 -0.4850169 5.502e-01
## Inshore_MK_Aug -0.312859 -0.914083 0.286588 -0.5818578 5.898e-02
## Inshore_MK_Sep -0.890765 -0.922617 0.415566 -0.1534626 3.959e-01
## Inshore_MK_Oct -0.575069 0.104661 -0.086529 0.6346904 -5.686e-01
## Inshore_MK_Nov -0.288229 0.145157 0.189211 -0.1743009 2.256e-01
## Inshore_MK_Dec -0.085250 0.867926 0.215879 0.0374703 -7.386e-03
## Inshore_LK_Jan -0.590510 0.586403 0.972785 -0.0952322 2.529e-01
## Inshore_LK_Feb 0.256754 0.554422 0.367560 0.1525029 -4.758e-01
## Inshore_LK_Mar 0.443808 0.051851 0.256576 0.3682254 1.555e-01
## Inshore_LK_Apr 0.399019 -0.759818 0.588020 0.2925842 -3.487e-01
## Inshore_LK_May 0.242058 -1.253166 1.122755 -0.0242360 -9.287e-02
## Inshore_LK_Jun -0.289364 -0.925207 0.621510 -0.3183745 1.259e-01
## Inshore_LK_Jul -1.178564 -0.905052 0.788844 0.0603697 5.616e-01
## Inshore_LK_Aug -1.064509 -1.114304 0.822035 0.3532532 -2.031e+00
## Inshore_LK_Sep -1.319835 -0.839383 0.681681 0.6565191 1.676e-01
## Inshore_LK_Oct -0.984068 -0.316540 0.678260 0.4606837 -2.698e-01
## Inshore_LK_Nov -0.907926 0.267668 0.461256 0.1593844 9.032e-01
## Inshore_LK_Dec -0.336361 0.163126 1.010217 -0.2489135 -2.481e-01
## Mid channel_BB_Jan 0.072858 0.277731 0.414876 -0.1977492 -9.959e-03
## Mid channel_BB_Feb 0.165595 0.288367 0.495426 -0.0970749 -1.843e-01
## Mid channel_BB_Mar 0.294564 0.205877 0.261025 -0.0868500 1.849e-01
## Mid channel_BB_Apr 0.349075 -0.053342 0.092296 -0.0322772 1.651e-01
## Mid channel_BB_May 0.328483 0.049237 -0.142553 -0.1319071 1.918e-01
## Mid channel_BB_Jun 0.029804 -0.167600 -0.151190 -0.2618361 -1.679e-01
## Mid channel_BB_Jul -0.203670 -0.003389 -0.530774 -0.1827288 1.985e-01
## Mid channel_BB_Aug -0.271202 -0.110264 -0.569853 -0.1970345 2.866e-01
## Mid channel_BB_Sep -0.548567 0.030415 -0.240530 0.2790886 -4.110e-01
## Mid channel_BB_Oct -0.360853 0.181900 -0.299527 0.1125300 -2.588e-01
## Mid channel_BB_Nov -0.099909 0.197236 0.035237 -0.1526986 2.212e-01
## Mid channel_BB_Dec -0.018258 0.369561 0.285468 -0.1815158 8.856e-03
## Mid channel_UK_Jan 0.219630 0.343525 0.329522 -0.0589944 -1.232e-01
## Mid channel_UK_Feb 0.352206 0.168912 0.517120 0.1301548 -3.850e-01
## Mid channel_UK_Mar 0.446768 0.001498 0.234928 0.1912685 1.164e-01
## Mid channel_UK_Apr 0.605343 -0.163994 -0.141341 0.3796071 1.577e-01
## Mid channel_UK_May 0.690927 -0.138947 -0.367168 0.4295415 3.816e-01
## Mid channel_UK_Jun 0.122324 -0.078102 -0.409761 -0.2671719 1.735e-01
## Mid channel_UK_Jul -0.167275 -0.083122 -0.534784 -0.1879445 -4.335e-05
## Mid channel_UK_Aug 0.111801 0.015420 -0.943865 -0.2843416 1.953e-01
## Mid channel_UK_Sep -0.108159 -0.191652 -0.670154 -0.1057227 -6.516e-01
## Mid channel_UK_Oct -0.344193 0.137987 -0.360602 0.1458208 -4.654e-01
## Mid channel_UK_Nov -0.206508 0.049982 0.188381 -0.1867746 -1.544e-01
## Mid channel_UK_Dec 0.035982 0.353246 0.262658 -0.2006087 9.268e-02
## Mid channel_MK_Jan 0.006826 0.623043 0.197904 -0.1301287 2.063e-01
## Mid channel_MK_Feb 0.377761 0.224931 0.427795 0.1525932 -3.160e-01
## Mid channel_MK_Mar 0.449739 0.171732 0.155446 0.2420828 3.164e-01
## Mid channel_MK_Apr 0.599260 -0.120431 -0.161504 0.2063767 2.403e-01
## Mid channel_MK_May 0.596198 -0.467539 -0.096949 0.3595340 3.313e-01
## Mid channel_MK_Jun 0.024454 -0.573247 0.026251 -0.3622452 -4.745e-02
## Mid channel_MK_Jul -0.371343 -0.598889 -0.053716 -0.4253270 1.448e-01
## Mid channel_MK_Aug -0.021205 -0.423305 -0.440860 -0.4178271 -3.502e-02
## Mid channel_MK_Sep -0.666429 -0.518907 0.074397 -0.0390396 -2.984e-01
## Mid channel_MK_Oct -0.167531 0.097697 -0.377172 0.0179380 -5.224e-01
## Mid channel_MK_Nov -0.074390 0.024545 0.087943 -0.2198889 -7.096e-02
## Mid channel_MK_Dec -0.035669 0.478599 0.301253 -0.0983641 2.443e-02
## Mid channel_LK_Jan -0.381032 0.492188 0.692426 -0.1012309 -4.719e-02
## Mid channel_LK_Feb 0.279374 0.499146 0.287640 0.1167638 -2.157e-01
## Mid channel_LK_Mar 0.215759 0.256958 0.195080 -0.0739025 9.371e-02
## Mid channel_LK_Apr 0.147505 -0.143263 0.159186 0.2862757 1.467e-01
## Mid channel_LK_May 0.223506 -0.672981 0.620235 -0.1874764 8.517e-02
## Mid channel_LK_Jun -0.077875 -0.297636 -0.027333 -0.3132011 1.342e-01
## Mid channel_LK_Jul -0.403600 -0.286449 -0.270734 -0.1919318 2.987e-01
## Mid channel_LK_Aug -0.388018 -0.540975 -0.018485 -0.3563497 2.447e+00
## Mid channel_LK_Sep -0.609209 -0.614102 0.180602 -0.1288225 -4.566e-01
## Mid channel_LK_Oct -0.282524 0.010178 -0.213631 -0.0391452 -3.898e-01
## Mid channel_LK_Nov -0.216250 0.056487 0.123276 -0.1844727 1.566e-01
## Mid channel_LK_Dec 0.007958 0.213739 0.390748 -0.1809511 -5.000e-02
## Offshore_BB_Jan 0.117931 0.188697 0.258795 -0.1384178 -2.301e-01
## Offshore_BB_Feb 0.266654 0.122053 0.522797 0.0005333 -2.691e-01
## Offshore_BB_Mar 0.277813 0.156643 0.259581 -0.0980915 1.912e-01
## Offshore_BB_Apr 0.074246 0.170608 0.090668 -0.2357099 2.227e-01
## Offshore_BB_May 0.184531 0.112029 -0.082850 -0.1769311 5.816e-02
## Offshore_BB_Jun -0.159521 0.072722 -0.243451 -0.1852854 2.180e-01
## Offshore_BB_Jul -0.105547 0.013917 -0.589505 -0.1635631 -5.489e-02
## Offshore_BB_Aug -0.400411 -0.012712 -0.541277 -0.0319487 4.332e-01
## Offshore_BB_Sep -0.206036 -0.048595 -0.575752 0.2423331 -6.258e-01
## Offshore_BB_Oct -0.091815 0.020853 -0.376238 -0.0736775 -5.454e-01
## Offshore_BB_Nov 0.104135 0.044067 -0.082375 -0.2587373 2.264e-01
## Offshore_BB_Dec 0.042602 0.211438 0.192773 -0.1650565 -8.193e-03
## Offshore_UK_Jan 0.147666 0.199716 0.272339 -0.1819025 1.643e-02
## Offshore_UK_Feb 0.249181 0.205046 0.587788 0.0346223 -4.138e-01
## Offshore_UK_Mar 0.232532 0.168046 0.230386 -0.1603731 1.991e-01
## Offshore_UK_Apr 0.095790 0.148234 0.109856 -0.2034063 1.614e-01
## Offshore_UK_May 0.142516 0.168077 -0.133043 -0.2349351 3.158e-01
## Offshore_UK_Jun -0.062876 0.035370 -0.298632 -0.2678513 2.615e-01
## Offshore_UK_Jul -0.154888 -0.072926 -0.544596 -0.2102515 7.839e-02
## Offshore_UK_Aug 0.033719 0.011813 -0.834714 -0.2830393 2.406e-01
## Offshore_UK_Sep -0.030449 -0.189680 -0.674330 -0.1217400 -6.031e-01
## Offshore_UK_Oct 0.007278 0.011817 -0.387918 -0.0976288 -6.068e-01
## Offshore_UK_Nov -0.038626 -0.015899 0.034318 -0.2891813 -1.899e-02
## Offshore_UK_Dec 0.026565 0.272532 0.157154 -0.1832805 9.688e-02
## Offshore_MK_Jan 0.055341 0.250933 0.307363 -0.2231282 3.783e-02
## Offshore_MK_Feb 0.262676 0.225349 0.643125 0.0653042 -4.477e-01
## Offshore_MK_Mar 0.202719 0.197666 0.237768 -0.1782254 2.423e-01
## Offshore_MK_Apr 0.215767 0.089472 0.065777 -0.1581338 1.762e-01
## Offshore_MK_May 0.118301 0.115507 -0.095814 -0.2116825 2.898e-01
## Offshore_MK_Jun 0.093702 -0.149516 -0.229294 -0.2879109 3.897e-02
## Offshore_MK_Jul -0.198118 -0.235912 -0.346218 -0.2364678 -9.018e-02
## Offshore_MK_Aug 0.016318 -0.075413 -0.695662 -0.2806353 6.241e-02
## Offshore_MK_Sep -0.118657 -0.327469 -0.359498 -0.2140151 -6.160e-01
## Offshore_MK_Oct -0.021657 0.030952 -0.425119 -0.0617342 -6.243e-01
## Offshore_MK_Nov 0.174361 -0.040168 -0.125784 -0.2315399 -1.994e-02
## Offshore_MK_Dec 0.082480 0.164161 0.248462 -0.1367257 -1.002e-01
## Offshore_LK_Jan 0.049944 0.294368 0.407952 -0.2244982 5.327e-02
## Offshore_LK_Feb 0.161692 0.288950 0.711888 0.0237256 -4.726e-01
## Offshore_LK_Mar 0.153579 0.138987 0.190385 -0.2025989 2.656e-01
## Offshore_LK_Apr -0.039603 0.124644 0.185897 -0.1915875 2.674e-01
## Offshore_LK_May 0.013459 0.116165 0.080236 -0.3129287 3.317e-01
## Offshore_LK_Jun -0.095265 0.005308 -0.238342 -0.2873527 2.462e-01
## Offshore_LK_Jul -0.274100 0.027011 -0.539493 -0.1069307 2.720e-01
## Offshore_LK_Aug -0.191214 0.041170 -0.616743 -0.1604437 4.367e-01
## Offshore_LK_Sep -0.101153 -0.220469 -0.348600 -0.1179020 -7.505e-01
## Offshore_LK_Oct -0.098867 0.042590 -0.318652 -0.1275626 -3.529e-01
## Offshore_LK_Nov 0.025507 0.030756 -0.036429 -0.2910472 1.905e-01
## Offshore_LK_Dec 0.117070 0.148438 0.206297 -0.0794536 -1.970e-01
## Oceanic_UK_Jan 0.122084 0.128552 0.151598 -0.1991850 -4.899e-02
## Oceanic_UK_Feb 0.267501 0.200894 0.580924 0.0174188 -2.136e-01
## Oceanic_UK_Mar 0.205907 0.061896 0.279773 -0.1556273 3.354e-03
## Oceanic_UK_Apr 0.235957 0.086173 0.193152 -0.1105785 -5.070e-02
## Oceanic_UK_May 0.308602 0.120778 -0.120869 -0.1807755 3.159e-01
## Oceanic_UK_Jun 0.086370 0.043022 -0.372061 -0.2684308 2.078e-01
## Oceanic_UK_Jul -0.029891 -0.079757 -0.588155 -0.2230675 -1.222e-01
## Oceanic_UK_Aug 0.131193 0.140414 -0.941440 -0.2782154 3.825e-01
## Oceanic_UK_Sep 0.140817 -0.223827 -0.634653 -0.0901625 -1.076e+00
## Oceanic_UK_Oct -0.053356 0.001929 -0.452062 -0.0815494 -5.247e-01
## Oceanic_UK_Nov 0.041568 -0.038498 0.050565 -0.2093171 -3.668e-01
## Oceanic_UK_Dec 0.118066 0.193551 0.068670 -0.1596925 1.308e-01
## Oceanic_MK_Jan 0.127618 0.189886 0.254430 -0.1846258 -2.328e-02
## Oceanic_MK_Feb 0.317716 0.130493 0.486674 0.0556385 -2.853e-01
## Oceanic_MK_Mar 0.123864 0.107886 0.180313 -0.2493826 1.761e-01
## Oceanic_MK_Apr 0.016379 0.170126 0.082657 -0.2350953 2.830e-01
## Oceanic_MK_May 0.034261 0.212923 -0.102523 -0.2294717 3.027e-01
## Oceanic_MK_Jun 0.055220 0.034612 -0.380733 -0.2781171 2.850e-01
## Oceanic_MK_Jul -0.039370 -0.058907 -0.581487 -0.1872127 -1.817e-01
## Oceanic_MK_Aug 0.005774 0.053505 -0.761004 -0.2179016 5.120e-02
## Oceanic_MK_Sep 0.143325 -0.134244 -0.638105 -0.1346925 -7.852e-01
## Oceanic_MK_Oct -0.035298 0.072413 -0.515501 -0.0967100 -3.312e-01
## Oceanic_MK_Nov 0.186024 -0.008573 -0.200512 -0.2785958 2.492e-01
## Oceanic_MK_Dec 0.065866 0.134077 0.226933 -0.1457790 -1.057e-01
## Oceanic_LK_Jan 0.107180 0.219970 0.316379 -0.2213347 1.135e-01
## Oceanic_LK_Feb 0.311173 0.212812 0.526674 0.0657674 -1.921e-01
## Oceanic_LK_Mar 0.139360 0.079101 0.145613 -0.2284421 1.434e-01
## Oceanic_LK_Apr -0.021097 0.138446 0.142587 -0.2539904 1.675e-01
## Oceanic_LK_May 0.032263 0.184316 -0.006528 -0.2868972 3.129e-01
## Oceanic_LK_Jun 0.010050 0.064792 -0.347627 -0.2520383 2.632e-01
## Oceanic_LK_Jul -0.091297 -0.048126 -0.461958 -0.1395289 -4.279e-01
## Oceanic_LK_Aug -0.083617 0.068552 -0.705873 -0.1619561 -2.814e-01
## Oceanic_LK_Sep 0.195644 -0.195147 -0.563994 -0.0508689 -1.127e+00
## Oceanic_LK_Oct -0.047990 0.030752 -0.403660 -0.1256421 -4.100e-01
## Oceanic_LK_Nov 0.235450 -0.018878 -0.194963 -0.2189184 3.944e-02
## Oceanic_LK_Dec 0.106917 0.128020 0.142436 -0.0908263 -2.494e-01# Checking if the PC axes are meaningful
eigenval <- CC.rda1$CA$eig # Here you will get the eigenvalues
sitecoord <- CC.rda1$CA$u[,1:2] # The site coordinates along PC1 and PC2
eig <- data.frame(eigenval)
eig$nb <- c(1:length(eigenval))
eig$prop <- eig$eigenval/sum(eig$eigenval)
eig# (Kaiser-Guttman)
par(mfrow=c(1,2))
barplot(eig$eigenval, main="Eigenvalues",las=2)
abline(h=mean(eig$eigenval),col="red") # average eigenvalue
legend("topright","Average eigenvalue",lwd=1,col=2,bty="n")
barplot(100*eig$prop,main="% of variance",las=2)
par(mfrow=c(1,1))
autoplot(CC.rda2, data = PCA.data, colour="Season",
shape="Sub_region",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Zone)+MyTheme+Season_colour+Season_fill
autoplot(CC.rda2, data = PCA.data, colour="Zone",
shape="Sub_region",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Season)+MyTheme
autoplot(CC.rda2, data = PCA.data, colour="Sub_region",
shape="Zone",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_grid(~Season)+MyTheme
autoplot(CC.rda2, data = PCA.data, colour="Season",
shape="Zone",
loadings = TRUE, loadings.colour = 'black',
loadings.label = TRUE, loadings.label.size = 3,
loadings.label.vjust = -1.0,
loadings.label.colour="black",
frame = TRUE, frame.type = 'norm')+
facet_wrap(~Sub_region)+MyTheme+Season_colour+Season_fill
Packages used
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